Volume 9, Issue 1 - Proceedings of the 57th Annual Meeting of the German Society of Biomedical Engineering (26 – 28 September 2023, Duisburg)

Manufacturing replicas for replacing missing or defective body parts also incorporates 3D technology, including scanning, image processing and printing. In the case of the ears, both aesthetics and functionality play a significant role. Based on these techniques, outer ear replicas were produced using 3D scans of human skulls in a rapid prototyping procedure for actual patients. Hand-held scanners delivered accurate images for post-processing of the mesh and for printing in a cost- and time efficient way. Use cases of patients demonstrate applicability, allowing the technology to be available also for the general population in the future.

The sound of food is of influence on how its flavour is perceived. Although rarely studied in psychoacoustics, cheese may have a resonating internal structure in the audible spectrum. It has been speculated that this structure or small bubbles that are formed as a result of fermentation are responsible for creating audible acoustic responses. The purpose of this study was to design a mechanical methodology to create audible acoustics from cheese samples and to quantify bubble presence in a sample. One hundred and two samples of mozzarella cheese with 1.5±0.4-cm 3 volumes were subjected to shear from a wetted steel blade, whilst orthogonal force, blade acceleration, and acoustic response were continuously monitored. In addition, micro-computed tomography was performed. It was found that under our measurement conditions, mozzarella was forced to squeak in 10% of the experiments, at fundamental squeak frequencies up to 2 kHz, which indicates that the acoustics come from a resonating porous structure, rather than from resonating bubbles. The micro-computed tomography showed a bubble density of 51 cm −3 . This low bubble density may account for the absence of a high-frequency component in the spectra analysed. Our results confirm the presence of small bubbles in squeaky mozzarella, but these generate frequencies much higher than those recorded.

Despite growing academic interest for dermal inking in contemporary society, the reason why skin tattoos remain visible for life has predominantly been studied from a biological perspective. In preliminary physics studies of ink it has been presumed that the hydrophobicity of its main constituent prevents further dilution of pigment dispersion and therefore may be a contributing factor to aforementioned lifelongevity. According to these early studies, ultrasound might change the hydrophobicity of microparticles. The purpose of this study was to confirm or refute the presence of hydrophobic components in carbon black tattoo ink and to relate sonication to such presence. Cuvettes with n-octanol, distilled water, and a droplet of unsonicated or sonicated carbon black ink were shaken, allowed to settle and subsequently photographed. The sonicated ink had been subjected to ultrasound during 5 min at a centre frequency of 1 MHz, a pulse-repetition frequency of 1 kHz, and a 10% duty cycle. The greyscale values in both parts of the cuvettes were averaged. The resulting ratio of light intensities was an indicator for the ink hydrophobicity. The intensity partition coefficient of carbon black ink was measured to be greater than 103 before sonication and less than 10 3 after sonication. Carbon black tattoo ink was found to be very hydrophobic. However, sonication was found to make the dispersion less hydrophobic. Influencing the hydrophobicity of tattoo ink might change the permanence of a skin tattoo.

This study was designed as proof of principle and safety test of the novel technique, the Immediate Stabilization System (ISS). The technique is designed to immediately stabilize polymer-augmented pedicle screws (PAS) in deficient bone and avoid complications of loosening pedicle screws at the bone-screw interface, especially in osteoporotic patients. A polymer sleeve was designed as augmentation to improve screw anchorage after drilling the screw hole. By applying ultrasonic energy, the polymeric tube was molded into the pores of the host bone forming a strong and uniform bond with the adjacent bone. The original screw was then implanted into the denser bony environment leading to an enhanced immediate stability. The ISS-treated implants were compared to conventionally placed pedicle screws in ex-vivo cadaver bones (2 sheep spines, n = 6 implants per spine, total 12 screws) and in-vivo in a spinal sheep model (Swiss alpine sheep, n = 5, 4 implants per animal, total 20 screws). The primary stability of ISS-treated pedicle screws was increased in ex-vivo bone (+24% insertion torque (IT)) and in-vivo (+32.9% IT) in sheep spine. Removal torque (RT) was lower in the in PAS tested for 8 weeks in-vivo. The ISS technology demonstrated improved anchorage of pedicle screws in ex-vivo cadaver bones as well as in-vivo studies in sheep spine.

Focused ultrasound on the thalamus is employed to relieve tremor in movement disorders. The treatment efficacy is currently assessed by subjective observation of patient tremor during hand movements. The purpose of this study was to create a simple quantitative method to assess tremor based on traditional pen-and-paper spiral drawing that could be employed during treatment and follow-up visits. Image processing was employed to automatically and rapidly clean scanned spiral and line drawings and to isolate the minimal part of the drawing that could provide sufficient data for tremor detection. Sobel edge detection and easily-interpretable statistics of the distribution of edge-angle orientations in the spirals relative to the spiral centres were used as tremor measures. The method was applied to scanned drawings of 122 tremor patients before treatment and in follow-up visits. Discrimination analysis of the tremor measure between drawings in the session before treatment and the follow-up sessions was carried out. The processing yielded correctly cropped and clean spirals. The tremor measure demonstrated consistent tremor reduction in the treated hand drawings in all five follow-up visits during the first year following treatment date in 76 patients from the study cohort. In this study we developed a computational method that rapidly computed tremor severity estimate in an explainable and clinically useful manner from a traditional pen-and-paper spiral drawing. This rapid quantitative and objective tremor assessment can easily be implemented during focused ultrasound treatment and replace the subjective assessment currently employed.

Automatic surgical skill assessment in robotic surgery based on video data is essential in facilitating faster learning curves for trainees while relieving expert surgeons from the time- and cost-intensive feedback process. Recent years have shown several advancements in this area by utilizing deep learning. While current research focuses on novel architectures, the influence of video-preprocessing on their performance remains unknown. In this work, we present the first investigation on the influence of video-preprocessing on deep learning-based surgical skill assessment. Thus, we integrated four preprocessing modules, i.e. Deblurring, Segmentbased Sampling, Optical Flow and the Combination of all of them, into skill assessment on the JIGSAWS dataset using a well-established network architecture. Despite all single preprocessing steps showing no clear improvement, the Combination of all steps showed higher median performance and lower variance. Furthermore, we performed frame-wise investigations on the influence of optical flow artifacts and their reduction in the combined setting. Our results highlight the potential of well-calibrated video-preprocessing for automatic surgical skill assessment.

Microbubbles with a negligible shell are of utmost importance in the study of harmonic ultrasound contrast agents. The purpose of this study was to collect and quantify experimental pulsation footage of gas microbubbles released from tablet matrix under sonication. Radii were measured as a function of time of 50 microbubbles that had been released from tablet matrix and were subjected to 3-cycle pulses of 1-MHz ultrasound with a 0.3-MPa peak-negative pressure. The size distribution was measured, as well as pre- and postsonication radii. The experimental footage showed proof of asymmetric buckling, but also of excursion-only behaviour. The former phenomenon has been attributed to a surplus of material accumulated on the microbubble-liquid interfaces, whilst the latter phenomenon has been associated with the presence of incompressible material inside the microbubbles. The postsonication resting radii of 41 microbubbles had become less compared to the corresponding pre-sonication radii. The opposite effect was observed with eight microbubbles in the same size range. This feasibility study confirmed that the gas microbubbles released from tablet matrix may pulsate asymmetrically. Thus, they might be suitable tracers for harmonic imaging.

Medical insoles are used to correct patient’s foot malpositions or to relieve certain foot areas from pressure. The required insole type depends largely on the presenting clinical picture and the individual’s needs. Accurate fit of medical insoles is critical to wearer acceptance, which is a necessary precondition where further serious injury is to be prevented. An end-to-end digital process offers the potential to better adapt insoles to the personalized patients’ foot geometry and pressure load. Furthermore, the whole gait-cycle could be included in the digitalization process and the adaption could consider special needs of different gait phases. This hasn’t been done, yet. For this purpose, a digital process chain was developed and prototypically tested in collaboration with an orthopaedist. 3D scans of the foot geometry in various loaded conditions were compiled and the corresponding gait analysis images were mapped. Both results were overlaid in a CAD program to create a model and identify the clinical picture. Adapted to the geometry of the foot, a volumetric model of the medical insole was built, individual stress zones were separated and filled with lattice structures of different parameters. The insole was 3D printed. The results of the present examination show benefits in using the loaded foot scan to model insoles, as malpositions can be checked automatically via standard (digital) tests. While it is possible to model and print medical insoles in one piece with differing strengths, there is limited information about the influence of the designed lattice structure on a specific printing result (i. e. the material behaviour).

Implant-associated infections are still a major issue in implant surgery. Biofilms tend to form on implant surfaces, like titanium, and can hardly be reached by the immune system. Additionally, systemic treatments with antibiotics often fail and cause severe side effects for the patient. Direct delivery of the antibiotic from the implant surface to the surrounding tissue is one approach to solve this problem. To realize this, the application of pH-sensitive coatings on implant surfaces, which release their cargo only if an infection arises, is a promising option. This can be triggered by the decrease of the pH value occurring in infected tissue. For such pH-sensitive systems with integrated drug loading capacity layer-by-layer coatings with weak polyelectrolytes can be used. Here, we present a coating applied on titanium substrates by dip-coating. As negatively charged polyelectrolyte polyacrylic acid, as positively charged poly(allylamine hydrochloride) is used. As an effective drug, the antibiotic ciprofloxacin is incorporated into the coating and the release profiles are recorded.

Theranostic agents are materials that act both as tracers during diagnostic imaging and as vehicles carrying and releasing therapeutics during treatment. Ultrasoundtriggered theranostic agents comprise shell-encapsulated microbubbles that pulsate during low-amplitude ultrasonic imaging and release their payload upon higher-amplitude sonication whilst simultaneously assisting in the permeation of target tissue. High-amplitude release may be undesirable due to unwanted side effects related to inertial cavitation. However, low-amplitude release from microbubbles typically requires thin encapsulating shells, which in turn may be permanently disrupted under diagnostic imaging conditions. The purpose of this study was to investigate the suitability for theranostic applications of a novel microbubble agent with thick shells composed of calcium carbonate. Hydrophobised calcium carbonate-encapsulated microbubbles of radii between 1.0 μm and 11 μm were subjected to short ultrasound pulses of 1-MHz ultrasound at acoustic amplitudes of 0.5MPa or less, which corresponds to low mechanical indices. During sonication, high-speed video footage was recorded at a frame rate of ten million frames per second. We observed pulsations but no gas release at a 0.1-MPa amplitude and intra-encapsulation fragmentation during sonication at a 0.3-MPa amplitude. At 0.5-MPa amplitude sonication, release was observed from more than 70% of the microbubbles in the field of view. This finding indicates that the microbubbles were stable scatterers during 0.1-MPa sonication, but instable vehicles during 0.5-MPa sonication. The pressures used in this study to observe release were too low to allow for unwanted inertial cavitation. In conclusion, therefore, the microbubbles studied were a promising theranostic agent whose contents could be released at moderate acoustic amplitudes.

The digitizing of medical education has been winning more support in recent years. One of the options is the implementation of Augmented Reality to complement or replace conventional methods in medical training and to create a more efficient learning environment for medical students and prospective staff in healthcare. In this work a Digital Tutor in Augmented Reality in medical training is evaluated with the aim of finding out whether it offers an additional value compared to conventional learning and teaching methods. Therefore, an application for medical training was arranged and a user survey with (n = 24) students from the Faculty of Life Sciences at the University of ANONYMIZED was carried out. The utilized application had two versions. One version included the Digital Tutor, who verbally delivered the instructions, and the other version included a text box with written instructions. The students evaluated these two applications through a questionnaire. Further, to validate this work an expert interview was conducted with a person in charge of designing concepts for the digitizing of education for the Faculty of Life Sciences at the University of ANONYMIZED. Through the survey and interview it was concluded that the Digital Tutor has an additional value compared to the text box. In the future the interactivity of the Digital Tutor with the students should be improved and a larger study should be done to further validate this outcome.

Bacterial keratitis is a widespread disease in which bacteria have infected the cornea. This can lead to blindness and should be treated immediately after diagnosis. However, antibiotics do not reach pathogens inside the cornea. Therefore, it is investigated whether UVB irradiation might be a feasible therapy that reaches and inactivates bacteria even deep inside the cornea. Thus, the cornea transmission of porcine eyes was measured to reveal a high UVB penetration. As Escherichia coli (E. coli) is one of the relevant pathogens, E. coli suspensions were irradiated by a 311 nm UVB lamp, to explore the antimicrobial impact of the UVB radiation. Finally, as a worst-case scenario, it was investigated whether E. coli behind the cornea was also inactivated. A cornea transmission of 36.4 ± 5.9 % was observed at 311 nm and an irradiation dosage of 1.9 J/cm 2 was found for a three log reduction of E. coli in suspension. The reduction of E. coli by 311 nm irradiation even behind the cornea could be confirmed. Therefore, by this approach, it should be possible to reach bacteria even deep inside the cornea by means of UVB irradiation as a fast keratitis therapy.

The paper discusses SkillsLab+, a medical augmented reality application developed to digitize the practical course SkillsLab. This application provides medical students with a digital tool to learn and apply practical hand movements, initial treatment, and diagnostic techniques. The otoscopy station, one of the stations in SkillsLab+, allows students to learn the correct use of otoscope inserts and perform case discrimination. The digitization of this station provides several advantages over traditional teaching methods. The implementation of the application with the Unreal Engine on the Magic Leap One headset is discussed. The application is modular, allowing the addition of new stations with ease. Anatomically correct models and 3D models of diagnostic tools provide the necessary realism and precision for the application. Learning and exam modes have been included in the application to enhance the learning experience. The evaluation of the application was conducted using a questionnaire completed by 79 bachelor and master students from the fields of medical informatics and digital biomedicine and health sciences. On average, 54.25% of students had a positive response to the digital otoscopy station in the SkillsLab+ application, while 22.71% had a negative response. The questionnaire results indicated that the digitization of the otoscopy station through augmented reality in the SkillsLab+ application was well-received by the students. The SkillsLab+ application digitizes the practical course SkillsLab and offers several advantages over traditional teaching methods. It provides students with a digital tool to learn and apply practical hand movements, initial treatment, and diagnostic techniques. The otoscopy station in SkillsLab+ allows students to learn the correct use of otoscope inserts and perform case discrimination. The digitization of this station provides students with a digital tool that can be used anywhere, any number of times. The evaluation of the application using a questionnaire showed that the application was well received by the students.

Background: Bladder cancer (BCa) is the second most common genitourinary malignancy and has a mortality of 165,000 deaths p.a. The diagnosis of BCa is mostly carried out using cystoscopy - the visual examination of the urinary bladder with an endoscope. White light cystoscopy is currently considered as gold standard for the diagnosis. Nevertheless, especially flat, small or weakly textured lesions, are very difficult to detect and diagnose. Objective: With the advent of deep learning and already commercially available systems for the detection of adenomas in colonoscopy, it is investigated how such a system - for colonoscopy - performs if retrained and tested with cystoscopy images. Methods: A deep neural network with a YOLOv7-tiny architecture was pre-trained on 35,699 colonoscopy images (partially from Mannheim), yielding a precision = 0.92, sensitivity = 0.90, F1 = 0.91 on public colonoscopy data collections. Results: Testing this adenomadetection network with cystoscopy images from three sources (Ulm, Erlangen, Pforzheim), F1 scores in the range of 0.67 to 0.74 could be achieved. The network was then retrained with 12,066 cystoscopy images (from Mannheim), yielding improved F1 scores in the range of 0.78 to 0.85. Conclusion: It could be shown that a deep learning network for adenoma detection in colonoscopy is ad-hoc able to detect approximately 75% of the lesions in the urinary bladder in cystoscopy images, suggesting that these lesions have a similar appearance. After retraining the network with additional cystoscopy data, the performance for urinary lesion detection could be improved, indicating that a domain-shift with adequate additional data is feasible.

Background: As part of a open surgical intervention, it is desirable to identify, track and count the surgical instruments used, in order to document and potentially optimize the process. In the past, various technologies have been proposed to this end, as e.g. RFIDs attached to or included in the instruments, contact-less identification by image analysis or (bar-, QR-) codes, or counting by human resources, but all of them having their individual drawbacks. Objective: We present and evaluate a new method to identify metallic-conductive surgical instruments using Low Frequency (LF) magnetic fields. Method: The investigated LF magnetic field approach is based on the IndLoc system developed by the Fraunhofer IIS - originally for metallic asset identification within the field of supply chain management - and is applied on small collection of 13 typical surgical tools (four scissors, two needle holders, four tweezers, two retractors, a bone curette) for open surgery. Results: 9 out of the 13 instruments can be correctly identified, two pairs (2 short and 2 medium tweezers) are too self-similar and could not discriminated correctly from each other. Conclusion: These initial experiments show that an LF-based identification of metallic surgical tools is feasible, but the system needs to be modified (with more and smaller coils) to increase the geometrical resolution to better distinguish between self-similar and potentially more objects.

Complex medical therapies can require a multitude of imaging modalities and are often supervised by a team with different medical backgrounds. This necessitates the conversion of medical data between technical systems and visualizations. In the case of stereotactic arrhythmia radioablation therapy (STAR-therapy) of the left ventricle, electroanatomical mapping, ultrasound (US) and computed tomography (CT) are the central imaging modalities that are needed for defining the target volume as well as for the examination and validation pre and post treatment. In the interest of developing a motion management system for STAR-therapy, a way to transfer information and visualizations between these modalities and to compare data from different patients is needed. For this purpose, we present a landmark-based approach for the generation of commonly used ultrasound views and the 17-segment model from cardiac computed tomography (CCT) data. The developed tool can already be used to aid the examination process by extracting function-based views from CT datasets, comparing them to live US imaging, localize CT structures within the 17-segment model or to transfer information like motion or strain data from one modality to the other. In the future, it will be used in the development of a live or predictive management system for cardiac and respiratory motion.

In this work, we investigate the possibility of automatically and objectively evaluate an initialized therapy for psoriatic arthritis patients (therapy monitoring). Near infrared fluorescence optical imaging (NIR-FOI) is used to visualise the microcirculation in the hands, giving insights into possible inflammations (e.g. synovitis). Due to the time consuming and expansive data acquisition, we firstly investigate the feasibility of NIR-FOI in this scope of application using retrospective data. To increase the data quality and harmonize the heterogeneous patient population, certain inclusion criteria must be met, leaving only a limited amount of data sets for data analysis. A neural network was trained to segment the hands into areas of interest (e.g. joints and fingers) and an image registration pipeline was developed to compare identified regions of interest among different patient visits. It is shown that data from different visits are not directly comparable and thus, needs to be normalised. Due to the limited amount of meta data, only a simple normalisation can be performed. Even though with this normalisation the therapy monitoring cannot be carried out, the normalised data shows an increased agreement to a medical label in comparison to unnormalised data. Therefore, we conclude that further research can lead to a robust therapy monitoring algorithm.

The integration of data gloves into Augmented Reality (AR) applications for medical training increases immersion and realism. By using devices with haptic feedback, better and more intuitive control can be achieved. This has implications for learning and exam preparation, but also enables new home-based learning and independence from physical models and presence. Smooth integration and connection of devices is essential for this purpose. This work aims to demonstrate native integration between an augmented reality headset, and a pair of data gloves. In the future, the use of AR and data gloves in medicine will continue to grow and technological advances will create even more realistic and immersive learning opportunities. The study will evaluate if the SkillsLab+ AR application and haptic feedback provide value to medical training and if they can be used effectively in medical education. The implementation shows promise, and the technology may have a future in other areas as well.

Purpose: With the ongoing work on the Health Level Seven (HL7) standards and the IEEE 11073 Serviceoriented Device Connectivity (SDC) family, the demand for open integration of medical devices in the operating room (OR) has become clear. Nevertheless, there are very few interoperable medical devices available to date. This work describes a practical example of connecting an intraoperative neuromonitoring (IONM) device with a radiofrequency (RF) surgical device based on SDC and with the hospital information system (HIS) based on HL7. Methods: Before starting the surgery, patient-related data must be entered into the neuromonitoring system. To minimize manual input, we integrated an automized patient data query based on HL7, which completes all other necessary data provided by the HIS after entering the patient identification number. During the surgery, while mapping neural structures in the situs using IONM, the parallel operation of RF devices for coagulation generates artifacts in the neuromonitoring signals, which makes a reliable interpretation of the IONM signals impossible. Therefore, we developed an IEEE 11073 SDC interface for the neuromonitoring device and implemented an SDC-based OR control panel. While placing the hand probe for mapping neural structures in the situs, the OR control panel suppresses the coagulation of the electrosurgical instrument and only reenables it, after the mapping has been terminated. After the surgery, the generated IONM report can be uploaded into the HIS using HL7. Therewith it is assigned automatically to the previously selected patient. Result: With the SDC- and HL7-enabled neuromonitoring system, we showed a practical use case of interoperable medical devices to optimize surgical workflow.

Physical models of bony areas are used in the medical environment for training, patient information or for various examinations. These are idealized average models which, however, do not reflect the individuality of a respective patient. Furthermore, the mechanical properties of the bony structure with cortical bone and cancellous bone are not considered in most models. A few manufacturers, such as SawBone, offer models and material blocks that approximate the properties of the bone. However, these are not patientspecific either. This paper presents a proposal for a method to create patient-specific physical bone models with CAD/CAM technologies that approximate shape and mechanical properties. The models are made of two different rigid polyurethane foams for cortical bone and cancellous bone. The methodical development includes the elaboration of a framework starting from the patient's CT data, through design derivation, to fabrication. Furthermore, a possible process for fabrication in the form of a demonstrator and its application in sawing tests will be demonstrated.

Photobiomodulation, the light stimulation of regenerative active cells, offers great potential for promoting tissue repair and regeneration. In this study, we show the design and fabrication of two microchannel devices for light stimulation of cell suspensions. The microchannels in the 3D-printed devices allow the uniform distribution of cells and exposure to light. Our experimental results demonstrate the ability of these microfluidic devices to improve light intake of cells in suspensions during photobiomodulation, thus enhancing the therapeutic potential.

While mechanical tracking is commonly seen in robot-assisted surgery, contactless gesture control leads to a more intuitive approach. Magnetic localization systems might be able to provide an untethered access to the necessary hand movement data. A model-based system exploiting a priori knowledge to improve the accuracy and robustness can be beneficial in this context. We present a proof of concept, in which the index finger of a digital twin of a hand is tracked with three simulated sensors. Based on the physiological composition, the finger is modeled as a kinematic chain with rotational degrees of freedom between the segments according to the corresponding finger joints. We applied an extended Kalman filter using this description to enhance position and rotation estimates for the sensors on the finger segments. We achieved mean absolute errors < 1 cm for the positions and < 10 for the local rotations with first simulations of a bending motion.

In this paper we present a novel noninvasive approach to estimate current densities in the heart from magnetocardiography. The proposed algorithm uses nested optimization to model current densities in equally-sized voxels of myocardial tissue. First-order Thiran all-pass filters are used to describe the propagation between voxels.We demonstrate feasibility of the algorithm for a noise-free single-layer simulation. However, challenges remain, such as addressing measurement noise and optimizing propagation velocity. Overall, this approach has the potential to complement or replace invasive catheter-based electrophysiological studies for localization of arrhythmogenic tissue.

Flexible endoscopes are widely used for diagnostic and therapeutic procedures on internal organs and tissue surfaces. Accurate positioning of the endoscope tip is critical for obtaining clear images and avoiding tissue damage. Proximity sensors can provide information about the distance to the surrounding tissue, which can help align the endoscope tip and navigate along curved paths. This paper presents a method of proximity measurement using capacitive sensors with active shielding to determine the radial distance between an endoscope and surrounding tissue. The capacitive sensor consists of four copper foil electrodes attached to a 9.5 mm diameter ring. Two different active shielding designs were investigated and compared to reduce the sensing angle and minimize lateral effects. Capacitance was measured as a function of distance using a linear stage. A MATLAB script controlled the linear stage and calculated the distance from the measured capacitance. The results showed that advanced active shielding could be useful to reduce the sensing angle (angular space where capacitance sensor detects capacitance changes.). This method provides a cost-effective solution for radial distance measurement in endoscopy, essential for improving diagnostic and therapeutic procedures.

Tissue-mimicking materials (TMMs) typically used for ultrasound phantoms include gelatin, agarose and polyvinyl alcohol (PVA). These materials have shown sufficient similarity in ultrasound parameters compared to human tissue. Despite their extensive use for years to generate ultrasound phantoms, no simple and easily reproducible way to generate complex, wall-less ultrasound flow phantoms has been introduced. Commercially available ultrasound flow phantoms are limited to simple flow geometries that do not reflect the complex blood flow in humans. Flow phantoms with complex geometries presented in scientific publications either have walls between TMMs and the flow channel, are limited to one material, or are complicated to produce. In this contribution, we present a method using 3D printing and soluble filament that allows for the reliable and consistent production of complex flow geometries with the typical materials used for ultrasound phantoms and without any walls.

Vibrational heating therapy is a type of physical therapy that uses the application of heat and vibrations to stimulate the body’s healing process. The therapy is believed to promote circulation, reduce inflammation, and relieve muscle tension. It is often used to treat conditions such as arthritis, fibromyalgia, and sports injuries. This paper presents the design-decisions for a device treating muscle soreness and to increase muscle strength through a combination of heat and vibration therapy to the upper leg. The system comprises farinfrared heat elements for heat transfer and two types of eccentric rotating masses (ERMs) and a linear resonant actuator (LRA) to vibrate muscle tissue. It discusses heating and vibration capability and performance. To optimize the mechanical effects of vibration therapy, the device must excite the muscle with an acceleration of more than 2 g and with a bandwidth as wide as the range of the muscle natural frequencies. For testing the heating and vibrational systems, they were tested in the same device on six different subjects with ethics permission from Technical University of Hamburg to obtain accurate results. Both ERMs were driven by DC power and yielded a bandwidth of about 30 Hz with a linear progression of acceleration over frequency, while the LRA was driven by AC power and achieved a bandwidth of about 24 Hz in the form of a bell curve. Also the selected heating element provides the desired temperature between 40 ∘C to 50 ∘C. Overall, the initial technical study showed promising measurement results and a mechanical effect in using this combination of heat and vibration therapy. As an outlook, the technical design of the next version of the device will include multiple actuators around the leg to provide more acceleration and aims to combine multiple LRAs with slightly shifted natural frequencies into one actuator module to extend the bandwidth.

Radiofrequency ablation is a minimally invasive technique for treating liver or kidney tumors, where precisely inserted ablation probes destroy the tumor tissue with induced heat around their tips. Automatic segmentation of needles from the abdominal computed tomography (CT) scans enables large-scale procedure evaluation and creates a baseline ground truth for automated planning approaches. Our method for needle segmentation enables the detection of multiple needles from unlabeled CT scans by sequential combination of two approaches, a classical method and a neural network. The former method detects intensity-prominent needles in 3D. The latter focuses on the detection of needles with lower intensity, that remained undetected by the classical method. A well-known encoder-decoder approach on axial slices of the CT scan gives a segmentation mask for the needles prominent in slices. To train the network, we create a synthetic dataset, adding needles to clean abdominal CT slices. Our network trained on a small synthetic dataset is able to generalize to clinical CT scans. The performance of the combined method is evaluated on ten anonymous patients from the Medical University of Innsbruck each with up to 18 needles inserted in the liver without any template.

Introduction: Biofilms are bacterial communities that transform into a state enclosed in an extracellular polymeric substance, which makes them less susceptible to antibiotics. Those bacterial formations often develop on metal implants and cause chronic infections. Due to the severely reduced impact of antibiotics against biofilms, the effect of electric stimulation (on its own and in combination with antimicrobials) needs to be further observed, as available studies indicate a positive effect. Methods: Therefore, this work examined the development of a six-channel current-controlled stimulation setup, which enables further in vitro research on the effects of electric stimulation on biofilms. The setup controls the desired stimulation current through the load to counteract electrochemical processes, which constantly change its resistive and capacitive properties. Results: Each channel of the stimulator is able to operate within an amplitude range of 50 μ A to 1 mA, a frequency range of 0 Hz to 1 kHz, and a pulse width range of 50 μ s to 1 ms. The current control provides a sufficient rise time of 3.3 μ s for three different stimulation modes: constant direct current (DC), pulsed DC, and biphasic-pulsed alternating current (AC). Furthermore, a graphical user interface enables the user to regulate and observe the stimulation on a computer to which the stimulator device is connected. Conclusion: The achieved variety of stimulation parameters in one device makes it possible to analyze the effect of different stimulation paradigms on biofilms and therefore enables more in vitro research which is inevitable to develop a sufficient treatment for patients with biofilm-infected implants.

Background: Due to an aging society and changing health behaviors, emergency room crowding has become a major problem in western health care systems. Empowering patients and health care workers to assess necessary and relevant information is critical to streamline clinical workflows. Health kiosks, designed for services like self-check-in or (ideally contactless) health self-assessment may be instrumental in solving this issue. Based on the collected data, automated workflows such as flagging critical patients, inducing specific diagnostics or early symptomatic treatment could be implemented. Objective: Using an AI-supported software, which visually analyzes and categorizes facial expressions, the emotional status of hemato-oncologic patients in a German oncology outpatient clinic was examined. Additionally a survey was conducted, evaluating the acceptance of such a self-assessment solution. Results: 98% of the participants were not stressed by the real-time emotion analysis. However, the current set of registered emotion categories was found to be only partially sufficient to adequately describe the emotional status of the patients. More importantly, 88% of the participants found such a system to be meaningful. Also, 84% of the participants agreed that such a self-analysis could be of potential assistance. No relevant generation- or gender-specific differences could be observed. Discussion: Automated analysis of patients’ emotional status can be a first step toward a more comprehensive assessment of the respective health status. Patients, in particular the elderly, approve to the vision and development of such a system. Next steps are a further improvement of the AI-based emotion recognition software with respect to more emotional states as well as the definition, inclusion and ideally contactless acquisition of physical biomarkers (as e.g. heart rate or respiratory rate) determining physical and mental well-being.

Positron Emission Tomography (PET) with 18Ffluorodeoxyglucose (FDG) is an important imaging practice in cancer diagnosis, staging, treatment planning, and response assessment. Accurate interpretation of PET results requires an understanding of FDG uptake in organs. The time activity curve (TAC) and standardized uptake value (SUV) are the two common tools to measure tracer uptake in regions of interest. The TAC provides information on glucose consumption dynamics, while the SUV involves measuring the amount of tracer taken up by a specific region or volume of interest, and then adjusting the measurement by the amount of tracer injected and the subject’s characteristics. In the current study, we measured the TACs and SUVs of the heart, brain, kidney, and muscle in ten mice injected with FDG over a period of 120 minutes. According to the TACs obtained in this study, the brain has a slower uptake than the other organs with a tendency to keep the FDG for a longer period of time. Also, the SUV of the brain showed a rising trend until the middle of the experiment with a sharp falloff afterwards. The pattern of the curves of the other three organs was almost the same. The findings of this study were in agreement with a similar study on humans and are explained by the metabolic activity and physiology of the organs studied.

We report on the development of a bioreactor system for mechanical stimulation of musculoskeletal tissues. The ultimate object is to improve the quality of medical treatment following injuries of the enthesis tissue. To this end, the tissue formation process through the effect of mechanical stimulation is investigated. A six-well system was designed, 3D printed and tested. An integrated actuator creates strain by applying a force. A contactless position sensor monitors the travels. An electronic circuit controls the bioreactor using a microcontroller. An IoT platform connects the microcontroller to a smartphone, enabling the user to alter variables, trigger actions and monitor the system. The system was stabilised by implementing two PID controllers and safety measures. The results show that the bioreactor design is suited to execute mechanical stimulation and to investigate the tissue formation and regeneration process. The bioreactor reported here can now be implemented in tissue engineering applications including tissue specimen.

Introduction: In cochlear implant surgery, the insertion of the electrode array (EA) into the cochlea is critical as its implementation can influence the preservation of residual hearing. Insertion tests are the primary method for basic research on parameters influencing the insertion and help to further improve the design of EAs and surgical techniques. With automated insertion devices close to clinical application, a consensus on optimal insertion parameters is needed, which requires reliable testing methods. Moreover, the limited availability of EAs needs to be considered. We propose a test setup that provides high repeatability and flexibility for various research questions. Methods: Design requirements for multiple types of experiments such as variability of insertion speed or trajectory as well as cochlear geometry guided the computer aided design of the test bench. Moreover, repeated insertions with the same EA were supposed to be possible. To evaluate its functionality, insertion tests into a 3D printed cochlea model were performed and recorded. Results: The central components of the test bench are a linear actuator driving the EA and a goniometer changing the orientation of the target - a cochlea model or a specimen. A force sensor can be mounted below the target to measure forces in its frame of reference. The experimental results show high reproducibility of insertion forces for recurring trajectories with a single EA. Conclusion: The test bench enables reproducible insertion tests with a high number of repetitions and reduced EA usage. This allows a more detailed investigation of broadly discussed influences on the insertion such as the insertion speed or trajectory as well as cochlear geometry and can thereby drive future EA development.

In research on cochlear implants, preclinical testing of newly developed electrode arrays and surgical tools is an essential procedure, which requires the availability of a suitable testing environment. For this purpose, human temporal bone specimens are most realistic, but their availability is limited and additional parameters such as insertion forces are hardly measurable. Therefore, the aim of this study was to develop a temporal bone phantom with realistic anatomical structures for intracochlear force measurement. The temporal bone was segmented from CBCT data of a human cadaver head. The segmented model was 3D printed with an additional artificial skin layer to enable the simulated use of surgical instruments such as a self-retaining retractor. A mechanically decoupled artificial cochlear model was realistically positioned within the temporal bone and was furthermore attached to a force sensor. The usability of the phantom was evaluated by performing automated EA insertions using an automated hydraulic insertion device. The experiments showed that the insertion forces within the cochlea could be measured without interferences from surrounding structures. Moreover, the artificial skin provided a rigid interface for the insertion tool. The new phantom is a realistic testing and training platform for cochlear implant electrode insertions with the advantage of measureable insertion forces.

A question which has been intriguing researchers since 1969 is whether covalently linked hormones or growth factors on surfaces are still biologically active i.e. still bind and activate cell surface receptors in the insoluble state. There is evidence of release or leakage of rhBMP-2 covalently linked to self-assembled monolayers (SAMs) on gold surfaces. After detergent wash (SDS), juxtacrine surfaces with rhBMP-2 appear functionally feasible at fM release.

The application of machine learning approaches in medical technology is gaining more and more attention. Due to the high restrictions for collecting intraoperative patient data, synthetic data is increasingly used to support the training of artificial neural networks. We present a pipeline to create a statistical shape model (SSM) using 28 segmented clinical liver CT scans. Our pipeline consists of four steps: data preprocessing, rigid alignment, template morphing, and statistical modeling. We compared two different template morphing approaches: Laplace-Beltrami-regularized projection (LBRP) and nonrigid iterative closest points translational (N-ICP-T) and evaluated both morphing approaches and their corresponding shape model performance using six metrics. LBRP achieved a smaller mean vertex-to-nearest-neighbor distances (2.486±0.897 mm) than N-ICP-T (5.559±2.413 mm). Generalization and specificity errors for LBRP were consistently lower than those of N-ICP-T. The first principal components of the SSM showed realistic anatomical variations. The performance of the SSM was comparable to a state-of-the-art model.

Atrial fibrillation (AF) is one of the most common cardiac diseases. However, a complete understanding of how to treat patients suffering from AF is still not achieved. As the isolation of the pulmonary veins in the left atrium (LA) is the standard treatment for AF, the role of the right atrium (RA) in AF is rarely considered. We investigated the impact of including the RA on arrhythmia vulnerability in silico. We generated a dataset of five mono-atrial (LA) and five bi-atrial models with three different electrophysiological (EP) setups each, regarding different states of AF-induced remodelling. For every model, a pacing protocol was run to induce reentries from a set of stimulation points. The average share of inducing points across all EP setups was 0.0, 0.8 and 6.7% for the mono-atrial scenario, 0.5, 27.3 and 37.9% for the biatrial scenario. The increase in inducibility of LA stimulation points from mono- to bi-atrial scenario was 0.91 ± 2.03%, 34.55 ± 14.9% and 44.2 ± 14.9 %, respectively. In this study, the RA had a marked impact on the results of the vulnerability assessment that needs to be further investigated.

The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.

Emotion intelligence is a popular research topic that stems to bridge the gap between human-machine interaction. The use of such systems varies, however, the goal remains the same, to develop a robust and accurate model for detecting and identifying emotions. Physiological signals, such as electrodermal activity (EDA) and heart rate (HR), when coupled with facial expression analyses can bolster the efficacy of the recognition system. To this end, the aim of this study is to analyse EDA and HR signals to identify reactions to emotional stimuli. The data used in this work was collected from different subjects taking part in a separate study on emotion induction methods. The obtained physiological signals were processed to identify the stimulus trigger instances and detect trends between the different administered emotional stimuli. The results showed that the EDA signal was able to pinpoint the emotional trigger with a root mean squared error (RMSE) of 0.9871. The HR signal showed inconsistencies, however, a clear trend was observed between the emotion reaction and relaxation phase. This preliminary study assessment highlights the possibility of implementing data fusion in emotion recognition systems.

Immersion and presence are important aspects of virtual reality (VR). Efforts are therefore being made to enhance these effects in order to increase the impact of VR and its transferability to reality. This is particularly important in research areas such as medical education and psychology. The development of haptic gloves has therefore increased significantly in recent years. These are immersive input devices designed to make virtual objects tangible. However, the research field is still very new and there is insufficient research on their handling and effect. For this reason, an application was developed to compare different haptic data gloves with different haptic technologies. A glove with vibrotactile feedback (Manus Prime X Haptic VR) and a glove with additional force feedback (SenseGlove) were used and compared in multiple tasks. These include finger tracking and gesture recognition, as well as hand tracking and the behaviour of gloves when grasping objects with and without haptics. The aim is to determine how well the haptics could be integrated and which is more accepted by the test subjects. For this purpose, a questionnaire has been designed to evaluate the effect of the haptic gloves on users. It aims to get a first impression of the tactile gloves available on the market and to advance research in the area.

The Diels-Alder (DA) reaction is one of the most studied thermoreversible reactions, suitable for the preparation of thermoreversible crosslinked materials. In this study, temperature- triggered liquefaction of DA hydrogels will be investigated. A DA model reaction is used to determine the retro- DA temperature of the two stereoisomers. A hydrogel based on furan-functionalized Poly-(N-(2-hydroxy-propyl) methacryl amide (PHPMA) and 4-arm-polyethylene glycol (PEG) with maleimide endgroups is rheologically investigated and liquefaction experiments are performed.

Focused ultrasound (FUS) can be used as a drug delivery application for localized chemotherapy to treat cancer. The effect of ultrasound-induced inertial cavitation is promising to trigger drug release from nanocarriers. To investigate this effect, usually, a passive cavitation detection setup is employed. However, applying such a setup is challenging for in vivo experiments, as the test object may need to be fixed inside the water tank. Thus, we present a prototype of a coupling device that could significantly simplify experiments. Since this setup favors undesired sound wave interference and their resulting exceedance of the Mechanical Index, we additionally investigated different signal lengths. The occurrence of standing waves at a signal length of 44 cycles can both be derived from a changing cavitation activity and our calculations. The appearing interference also results in a mean increase of the cavitation activity by ≈ 5.1 %, verified by our experiments as well.

Background: The examination of lymph nodes (LNs) regarding metastases is vital for the staging of cancer patients, which is necessary for diagnosis and adequate treatment selection. Advancements in digital pathology, utilizing Whole-Slide Images (WSIs) and convolutional neural networks (CNNs), pose new opportunities to automate this procedure, thus reducing pathologists’ workload while simultaneously increasing the accuracy in metastases detection. Objective: To address the task of LN-metastases detection, the use of weakly supervised transformers are applied for the analysis of WSIs. Methods & Materials: As WSIs are too large to be processed as a whole, they are divided into non-overlapping patches, which are converted to feature vectors using a CNN network, pre-trained on HE-stained colon cancer resections. A subset of these patches serves as input for a transformer to predict if a LN contains a metastasis. Hence, selecting a representative subset is an important part of the pipeline. Hereby, a prototype based clustering is employed and different sampling strategies are tested. Finally, the chosen feature vectors are fed into a transformer-based multiple instance learning (MIL) architecture, classifying the LNs into healthy/negative (that is, containing no metastases), or metastatic/positive (that is, containing metastases). The proposed model is trained only on the Camelyon16 training data (LNs from breast cancer patients), and evaluated on the Camelyon16 test set. Results: The trained model achieves accuracies of up to 92.3% on the test data (from breast LNs). While the model struggles with smaller metastases, high specificities of up to 96.9% can be accomplished. Additionally, the model is evaluated on LNs from a different primary tumor (colon), where accuracies between 62.3% and 95.9% could be obtained. Conclusion: The investigated transformer-model performs very good on LN data from the public LN breast data, but the domain transfer to LNs from the colon needs more research.

The emerging desire for implantable devices assisting individuals’ medical treatments requires their connection to systems placed outside and inside the human body. Their exchange of crucial information on health parameters at different locations may allow them to optimize the patient’s therapy by evaluating the received and transmitted data. Electromagnetic communication methods are the current implementation of choice for such applications. Yet, this kind of communication is often limited to shorter distances due to their restrictions on safe electromagnetic levels, causing no damaging heat dissipation in human tissue. In contrast, employing ultrasound as an alternative method will allow for long-distance communication in the same medium by simultaneously guaranteeing safety requirements for the patient. In this contribution, we will demonstrate a proof-of-concept for which we modulate ultrasonic carrier signals with the help of a low-power consumption microcontroller TI-MSP430. For this, two identical non-focused ultrasonic transducers will be used to receive and send messages through a tissue-mimicking phantom placed in a water tank, indicating the potential of ultrasonic signals as the carrier of information.

The global shortage of healthcare staff has led to high workloads and subsequent risks to patient well-being. One of the professions affected is that of the scrub nurse. Robotic scrub nurse systems have the potential to reduce workload and to assist in handling surgical instruments. Existing approaches mostly use two-finger grippers or electromagnetic grippers. However, it is assumed that a granular jamming gripper is more suitable for handling various surgical instruments, regardless of material and shape. A gripping unit based on a granular jamming gripper and attached to a robotic arm is presented. For evaluation, six surgical instruments were repeatedly gripped and transported. The granular jamming gripper was found to be suitable for picking up and transferring most instruments, however, handling very flat instruments turned out to be challenging.

Denoising of time series is still a challenge, especially when the spectral components of wanted signal and noise overlap. However, denoising is an inverse problem and its solution is of often ambiguous. Standard methods often use predefined functions, e.g., sine waves, to decompose a time series into wanted and unwanted parts. More recent methods calculate the basis for the representation of a time series by the time series itself. Such a method is singular spectrum analysis (SSA). SSA uses Hankel matrix embedding of a time series and singular value decomposition to determine wanted and unwanted components, e.g., noise. The wanted components are then used to compute the wanted part of the time series. Here we present a method that provides an extension of SSA for analyzing and - in particular - denoising multi-channel time series, i.e., multi-channel SSA-based denoising of multi-channel time series (MSSAD). The performance of the new method, which is based on Hankel embedding and tensor decomposition, is demonstrated on a 12-lead ECG.

Pacemaker spike detection is an important step in monitoring paced patients. Capacitive ECG facilitates unobtrusive monitoring of subjects during daily routines such as driving. Robust algorithms are required to deal with low signal quality and artifacts, e.g. by employing fusion of multiple signal channels. Due to the low signal-to-noise ratio of the measurement, there are limitations to detection accuracy compared to conventional ECG monitors. Especially low voltage stimulations such as bipolar pacemaker spikes are hard to detect. We present a convolutional network approach to improve on recent signal processing algorithms.We show a realistic evaluation of its performance using leave-one-subject-out cross validation (LOOCV), its dependence on the size of the receptive field, and an estimation of an upper performance bound.

Cognitive performance is an important aspect of life. Not only does thinking enable us to act, there is also an interaction between cognitive performance and mental health. It is therefore increasingly important to train and practice one’s cognitive abilities and benefit from brain plasticity. According to Vester’s model of learning types visual, auditiv and haptic impression are imporant for learning. An existing memory training application, which transfers the concept of the game Pairs with personal memories into virtual reality (VR), has now been supplemented with a haptic impression. The original application already establishes the visual and auditory perception channels. With new haptic hardware constantly appearing on the market, the Tactsuit X40 haptic vest from bhaptics has now made it possible to include a third perceptual channel with a wide variation of haptic sensations on various parts of the upper body. Each playing card is associated with an individual image, a piece of music and a vibration pattern. All of which are designed to help the user remember the position of the card. Each of these aspects can be freely enabled and disabled. The result of this work is an application which aims to find if and how strongly haptic impressions influence memory performance. By taking more types of learning into account, we hope to achieve a better memory training. A study has been designed to evaluate the specific aspects of the application and is currently being carried out. In addition to the self-generated haptic patterns, it is also possible to generate haptic impulses based on audio sources. These could possibly provide further support in this application.

We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a lightweight, easy-to-apply, and fast-to-train approach that can be adapted to changes in image material though efficient retraining. Moreover, the framework is able to adapt to various input sizes making it robust against changes in resolution and is not tied to specialized hardware, which allows efficient application on standard laptops or desktops without GPUs. We benchmark two distinct models, a single structured classifier and an ensemble of structured classifiers, against a U-Net, evaluating overall performance and training speed. The framework is versatile and can be applied to multi-class semantic segmentation. Our study shows that the proposed framework can effectively compete with established deep learning methods on diverse datasets in terms of performance while reducing training time immensely.

Introduction. Computational models of optic nerve stimulation can allow to estimate the neural response of optic nerve fibers electrical stimulation and thus can be exploited to tune stimulation parameters to obtain a specific target perception. In principle, such tuning should be performed in an automatic way, so that the chosen stimulation parameters minimize some given cost function related to the quality of resulting the visual perception, but the use of such automatic methods is still under study and no satisfactory solution is available yet. In the absence of automatic methods, stimulation parameters are customarily set via manual tuning, which can be extremely time-consuming if performed in a non-principled way. Methods. We build biophysically-accurate hybrid models of monopolar and bipolar electrical stimulation of optic nerve fibers to study how the fibers firing rates depend upon the stimulation parameters. Results. In the case of monopolar sinusoidal stimulation of optic nerve fibers, we show that the amplitude of stimulation controls the size of the recruited cluster of fibers, and that the frequency controls their firing rate, independently. Instead, for bipolar stimulation, we show that when cross-talk is non negligible it is very difficult to obtain rules of thumb linking the firing rate of target fibers to stimulation parameters. Conclusion. We show that, if the stimulation amplitude is kept such that neighboring stimulating sites do not produce cross-talk, it is possible to reconstruct visual scenes “pixel-by-pixel” without needing any optimization process. If on the contrary current steering is required and cross-talk is non negligible, then it is very difficult to obtain rules of thumb and the development and use of automatic optimization techniques should be preferable.

Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cranial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive preprocessing. Methods: We propose a multi-height-based classification approach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classifiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects. Results: The multi-height-based approach improved classification for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89% and a mean F1-score of 0.75. Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical parameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients.

The MDR states the objectives of the law in the preface. There you will find, among other things, a consideration of small and medium-sized enterprises as well as high health protection and an innovative effect. Therefore, the question arises to what extent these goals have been achieved so far or can be achieved in the foreseeable future. To this end, a systematic literature review was conducted to present the perspectives of various stakeholders in the healthcare sector. Critical situations certainly arise for certain manufacturers, Notified Bodies and certain patient groups. The adopted extension of the transitional periods could be helpful but will not solve all problems.

Digital health applications are digital medical products (e.g. apps, web applications) that are prescribed to insured persons by physicians or psychotherapists and reimbursed by health insurances. For manufacturers, fulfilling the regulatory requirements for approval of a digital health application is challenging. This can be seen in the number of pulled and rejected applications from manufacturers for listing in the register of digital health applications. As of May 2022, a total of 61 applications have been pulled back by manufacturers and 8 have received negative decisions from the German Federal Institute for Drugs and Medical Devices. Manufacturers want support to fulfill the requirements for approval of a digital health application. Based on a literature review and a sighting of relevant regulations, a roadmap was developed to represent the approval process for a digital health application to manufacturers in a visual and simplified flowchart. The roadmap consists for example the submission of the application, the verification of the application and listing of the digital health application in the register. The roadmap simplifies the approval process of a digital health application, enables early identification of requirements, and helps determine the right strategy. A field trial is essential to confirm its effectiveness and improvements are needed based on feedback. The roadmap is being tested with startups and small businesses to evaluate its applicability in different contexts.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of Non-Hodgkin’s Lymphoma, presenting a great challenge for treatment due to its highly heterogeneous nature. DLBCL is diagnosed based on microscopy images of patient tissue samples. To help gain a better understanding of DLBCL, we developed an automated computer vision method to analyze morphological and color-based information within patient biopsies. We analyzed a dataset of whole slide images of DLBCL by segmenting individual cells and representing cell morphologies through a set of engineered features. The features were evaluated using a variety of visualization and machine learning (ML) classification techniques. Current state-of-the-art deep learning methods use images as the input in classification tasks achieving high performance but lacking in interpretability. A big challenge lies in finding out what features the pixel-based deep learning methods utilize in prediction. Here, we present a technique that not only yields high prediction accuracy but also provides insights into which of the features are key for prediction. We show that the color-based features have the highest importance for cell classification, allowing for the accurate identification of various cell types with an accuracy of 84% in a multi-class and 91% in a binary classification. Our results provide valuable insights for exploring cell image datasets to gain an in-depth view of the tumor microenvironment.

Surgical tool detection is an important aspect for recognising surgical activities and understanding surgical workflow. Laparoscopic videos represent an information source that can be used for recognising surgical tools. However, manual labelling of tool incidence and location in such data is extremely time intensive. Therefore, weaklysupervised approaches have been developed to perform tool localisation. In this study, three types of spatial pooling methods were implemented to evaluate the influence of each method on the performance of weakly-supervised model. The best achieved performance was a mean average precision (mAP) of 94% for tool classification and a f1-score of 70% for tool localisation. Experimental results showed the importance of selecting an appropriate pooling function to enhance model performance.

Sleep apnea syndrome (SAS) is a common sleeprelated breathing disorder characterized by recurring cessations of airflow during sleep. The gold standard for diagnosing SAS is polysomnography (PSG) which requires the patient to spend one or several nights in a sleep clinic. A PSG involves a significant amount of contact-based sensors, which leads to discomfort and deviations in sleep behavior. In this work, a contactless, multispectral camera-based approach for the autonomous detection of events of nocturnal airflow reduction is presented. The detected events are further employed in estimators of sleep diagnostic metrics, such as the apnea-hypopnea index (AHI) and the SAS stage. The AHI estimation resulted in a Pearson correlation coefficient of r = 0.9993. The SAS stage estimator correctly predicted the SAS stage for all three recruited patients.

When modeling High-Frequency (HF) surgical applications the choice of the right dielectric tissue parameters is a key factor for success. The water content of biological tissue is of particular importance since the dielectric tissue properties highly depend on it. Therefore, we investigate a simple and fast method for the determination of relative tissue water content using small samples of an ex vivo porcine liver. To identify the water content, we utilized a desiccation and weighing experiment. It is assumed that the weight loss can be directly attributed to the loss of water in the case of desiccation. Based on the ratio between the initial and dry weight of a sample, the relative water content can be determined. The drying procedure lasted 28 hours, with 16 small samples taken from an ex vivo porcine liver. At the end of the drying procedure, a relative tissue water content of 69.6 % ± 1.1 % was observed. Using a curve fit to the measured data, it can be assumed that approximately 99.3 % of the tissue water content had evaporated by the end of the drying process. With the drying and weighing method used, we were able to determine the water content of the ex vivo porcine liver relatively easily. However, our results also suggest that optimizations of the applied method should be considered for future measurements. Especially, if a faster determination of the water content is required, which in a broader sense should afterward serve for the validation of a simulation model.

To cope with the increasing demands in care due to the aging society and the simultaneous lack of professional caregivers， a technical assistance system can help to monitor elderly people in their own homes and to support professional caregivers and caring relatives. The technical assistance system consists of a smart sensor with an omnidirectional camera on the ceiling of each room and additionally, other smart home sensors in an apartment for elderly persons. Based on smart sensor data, the positions, poses, and activities of the patient are detected with the help of machine learning (ML) techniques. In this work a temporal behaviour model of the patient is developed to recognize activities of daily living (ADL) such as "eating", "sleeping" or "emergency". For this, the actions (e.g., walking, sitting) and the location in the current room of the patient, as well as the data of other sensors in the apartment are used. This input data is fed into a trained decision tree of depth 14, which ultimately determines the patient’s activity. The accuracy for detecting activities of daily living with the decision tree is 96.47%, where the activities can be detected in real-time.

More and more people are changing their lifestyle to a vegan diet. On the one hand, a vegan diet can help reduce the risk of nutrition-related diseases, but on the other hand, the vegan diet itself poses the risk of malnutrition. Therefore, nutrient intake should be managed and any nutrient interactions should be considered. The aim of this paper is to work out a development strategy for a nutrition app for a vegan diet. With the help of a market analysis, unique selling proposition were identified and regulatory requirements have been determined. It could be shown that there is currently no adequate app that is approved as a medical device, focuses on the vegan diet and considers nutrient interactions. The necessary regulatory requirements could be identified. There are many apps available, but without medical device approval. From the authors' perspective, this would be necessary to ensure a high level of safety and health protection for users.

According to [1]-[3], the healthcare systems are suffering from severe personnel shortages with a lack of medical care workers, operating room nurses, and surgical technicians. This deficiency will become worse in the upcoming decades. Automatization of highly standardized procedures could be a promising approach to address this issue. We designed a new concept for the automated handover and return of surgical instruments to the surgeon in an operating theater scenario to relieve operating room personnel from their workload. In this setup, the surgical instruments are delivered pre-sorted in an upright position by the sterilization unit in an instrument basket or drum. A single-use, doublelayered, brushy conveyor belt system mounted on a robot arm acts as the connector between pick-position (instrument basket) and individually pre-definable hand-over-position. The system is placed next to the table inside the operation room (OR). Based on voice commands, the conveyor intake moves over to the selected instrument and delivers it to the surgeon in a fast and short motion. After use, the surgeon can feed the instrument into the intake at the handover position and place it back in the instrument basket. The robot arm allows precise maneuvering of the intake and output position. The concept was realized as a minimal-viable product and will be evaluated for further improvement.

In large animals, fractures of the upper long tubular bones such as forearm and humerus as well as lower and upper leg, continue to represent a major surgical challenge. An implant and a corresponding surgical procedure for intramedullary osteosynthesis in fractures of long bones were developed at fzmb GmbH. Fixation of the implanted nail to the distal drill holes has proven to be difficult and requires a high level of X-ray monitoring. Therefore, a camera-marker system (CMS) was developed without the need for X-ray monitoring during drilling. The test was performed on 10 equine femora. The results showed that 8 of 10 implants could be securely fixed and no complications occurred in the experimental procedure. For the first two implants, the distal drill holes could not be hit in the first attempt. This is due to the circumstance, that the handling of the CMS needs to be practiced to hit the distal drill holes accurately and appropriately, resulting in a hit rate of 80 %. However, the described method has great potential and does not require X-ray exposure.

The cranial cruciate ligament (CCL) rupture is a prevalent injury in dogs. A consequence of a cruciate ligament rupture is instability in the affected knee joint. A veterinary, mostly surgical treatment of the cruciate ligament rupture is usually unavoidable. The suitability of an arthroscopic surgical method with ligament replacement material was investigated. The stability of the knee joint was determined several times during 1,200 passive robotic motion cycles with movement radius between 90° flexion and 140° extension. The stability condition was measured by triggering the drawer test. After 300 motion cycles, the drawer test could be triggered (positive drawer test). In the following movement cycles up to 1,200 cycles, the drawer test could also be triggered. However, no significant differences occurred between these triggered drawer tests. The ligament replacement material showed no damage and no loosening after the tests. The first results showed that the developed arthroscopic surgical method could be a promising approach for the surgical treatment of cruciate ligament ruptures in canines.

The cochlear implant (CI) restores hearing to patients with severe to profound sensorineural hearing loss by stimulating the spiral ganglion neurons (SGN). Due to the inner ear anatomy, a fluid-filled gap remains between the SGN and the electrode array. This gap impedes focused stimulation and thus optimized performance with the CI. Regenerating neurites may bridge this fluid-filled gap for a direct nerveelectrode- link but require a supportive matrix. Spider silk might be a suitable candidate for this support. Therefore, silk fibers were tested for biocompatibility with inner ear neuronal tissue, suitability as neurite outgrowth matrix and applicability to the CI-electrode array. Dragline silk from female Trichonephila spiders was woven around a frame and autoclaved. Spiral ganglia of young rats were prepared, cut and placed in a drop of medium on the parallel silk fibers for pre-cultivation to support adherence before remaining medium was added. After 5d of cultivation, cells were fixed and immunocytologically stained. For CIapplication, silk was wrapped around a silicone dummy and a one-sided spacer. Opposite the spacer, alginate-hydrogel was applied for silk-fixation and -shielding. After gelation, silk loops were cut in the middle to form protruding threads. Cells showed a high degree of migration and neurite regeneration along the silk fibers and in some cases the outgrown neurites directly contacted the silk. It was possible to fix the silk to the dummy as protruding fibers. The tested spider silk is compatible with the inner ear tissue in culture, supports cell growth and seems to be an attractive material for neurite contacting. Application to the CI as protruding fibers is possible, making it a promising candidate for structural support to bridge the nerve-electrode-gap.

Increasing patient safety during operations is an inevitable topic in anaesthesiology. The cockpit strategy presented by Vogelsang et al. addresses this issue with elements such as closed loop communication and the use of checklists. In this paper, the use of checklists is taken up further. An AR application is presented that augments checklists for anaesthesia induction into the user’s field of view. The application is to be implemented in teaching and help prospective physicians to internalise the workflows in anaesthesiology more quickly and sustainably. A working prototype has been created and loaded onto the HoloLens 2. The prototype is being assessed and evaluated by an anaesthetist. The feedback will be included into the following development steps. The application will be further adapted to the needs and requirements of the users. In order to evaluate the usability and the user experience, students and medical professionals should test and evaluate the application repeatedly.

This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The system comprises five calibrated sensors and a mobile app to measure lower body orientation during gait and stair climbing. Eight healthy participants were involved in the experiment, each performing ten repetitions to analyze hip and knee flexion angles. The IMU system demonstrated significantly lower mean square error than deep learning-based approaches and comparable results to the Vicon system, indicating its potential for clinical and research applications.

PREP are typical signal courses in the EEG, which are used to investigate the influence of neuropathologies or, for example, post-traumatic headaches on pain processing. The medically meaningful peaks are detected manually for further analysis. To develop a more time-efficient method, an automatic, latency-independent way of detection was created using the cross-correlation function, which generates a sample response for each data set. The comparison of automatic and manual methods shows advantages and disadvantages of both methods, but no significant deviation of the automatic method can be found.

This paper presents our approach to a privacy preserving person detection algorithm on edge computing devices. It utilizes a commonly used neural network architecture (VGG16) to encode an image by passing it through a fixed number of layers of the network on the edge device. The resulting feature vector can then be transmitted over the network to a more powerful computer (e.g. in the cloud) to be passed through the remaining layers of VGG16. As a result, the transmitted feature vector is only an abstract representation of the image. However, our research has shown that it is in fact possible to reconstruct most images by their respecting feature vectors if a potential attacker can tap into the network transmission by using a decoder network. Our approach to mitigate that risk is to preprocess the transmitted feature vector randomly by different manipulation methods. We used methods like mean filter or random null value insertion to manipulate the feature vector before it gets transmitted and show that those methods are able to counter the reconstruction capabilities of a decoder network while still preserving the capabilities of the original VGG16 network. The actual VGG16 network was used for object detection.We used the faces in the wild dataset and utilized two different approaches to confirm our approach. First, we used the VGGFace-Network on the decoded images and tried to let it identify people from the former mentioned dataset. The second approach used a group of 26 participants who had to match the decoder image to one of five images. In our experiments, we found different combinations of manipulation and number of layers on the edge device to preserve the detection capability of VGG16 (e.g. object detection) while preventing VGGFace and the participating group from identifying the shown people.

Patients suffering from Wilson’s disease accumulate copper in their body. Copper load may become visible as Kayser-Fleischer (KF) rings - colorful copper-sulfur granules in the base of the cornea. Reoccurrence of KF rings in patients under anti-copper medication may be a sign for ineffective treatment. This work proposes a hand-held sensing system based on infrared reflectivity measurement of the cornea as a potential method for regular at-home progression monitoring. The intensity of the LEDs used is safe for operation on the human eye. As proof of concept, reflectivity was measured when placing the sensor onto samples of white paper that had copper tape of different sizes centered within the sensitive area. Further, reflectivity was determined for different solutions of copper sulfate in saline that were poured into adapter bowels fitting the sensor head, with Cu2+ concentrations of the samples deducted from literature. While the prototype system can be used to differentiate between two copper strips of 6 and 12 mm2 or larger, a different measuring principle would be needed to also determine the concentration of copper sulfate solutions. Since these two in-vitro models cannot fully represent the complex in-vivo molecular conditions of patients’ corneas, additional measurements on patients’ eyes are necessary to evaluate the potential of the proposed sensing system.

Musculoskeletal and nervous system injuries and strokes can influence the ankle in a way, that rehabilitation exercise is inevitable to regain a patient’s quality of life. However, increasing needs for physiotherapists and complex machinery for independent rehabilitation limit the patient’s possibility of experiencing proper care. One approach to address this issue is developing an affordable, safe, and cheap treatment solution. This paper presents a mechanically actuated ankle rehabilitation mechanism, which incorporates the use of the patients’ upper limbs to work on the ankle's range of motion. The mechanism consists of a Four-Bar linkage (FBL) to translate rotary motion into an oscillating angular displacement of the ankle. It provides the base for gamified ankle rehabilitation, acting as an input device for the game, controlled by the user; adopting the mechanism with the commercial games and required steps for designing a game for rehabilitation purposes.

Introduction: With advancements in sensor and communication technologies, sleep monitoring is moving out of specialized clinics and into everyday homes. Extracting sleep-related data using far less complicated tools and procedures is possible than polysomnography. Respiratory and cardiovascular data are extracted from the signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) to identify the aberrant respiratory events of apnea/hypopnea as well as to estimate sleep parameters. However, due to the different sleeping positions, such systems lack accuracy and/or complicated sensor network topology. In this work, we proposed an optimal topology of forcesensitive resistor (FSR) sensors to simplify the system design by identifying the region of interest for estimating cardiorespiratory parameters with minimal error. The sensors are deployed under the mattress and located on the bed frame. Methods: We proposed a low-cost, unobtrusive, non-invasive, and reliable solution with robust signal processing algorithms for cardiorespiratory measurements and automatic signal validation based on signal quality. The solution is established based on a multi-physical layer (MPL) and sensor interfaces coping with the embedded system’s specifications, and signal processing is performed onboard with two independent and simultaneous pipelines for heart rate and respiratory rate using discrete wavelet transform (DWT) and bandpass filter, respectively. Results: We identified the three most contributing FSR sensors forming a triangle shape covering the left upper side of the subject (in the supine position) as the region of interest. We reduced the mean absolute error (MAE) to as low as 3.94 and 2.35 for heart rate and respiratory rate. Conclusions: The approach with the topology of triangle-shaped performs appropriately in estimating the cardiorespiratory parameters in all four regular sleeping positions, i.e. supine, prone, left lateral, and right lateral.

One of the difficulties in sorting extracellularly recorded neuronal action potentials, known as spikes, is noise from several sources superimposed on the target spike. For example, the activity of other neurons is often overlaid on the recorded spikes, denoted as neuronal noise interference (NNI), and interference from neighbouring neurons firing at the same time is known as overlapping spikes. In addition, the noise induced by the recording device is known as Gaussian white noise (GWN). Thus, the appropriate signal denoising process of extracellular recordings is an extremely challenging task, where the classical filtering approach may fail specially in removing the overlapping spikes due to the similarity between the spike waveforms of the neighbouring neurons and the wanted single-unit waveform. This issue is tackled in this work by training regularized denoising autoencoder (RDAE) model with the average spike-waveforms of previously identified single-units, denoted as ground truth. To capture the distinct features of the wanted single-unit waveforms, and weight regularization were employed at the encoder/decoder of the RDAE, respectively. With a suitable regularization parameter, the -RDAE model outperforms the -RDAE model. In general, RDAE shows extraordinary performance not only in removing the NNI and GWN, but also the overlapping spikes.

Correcting lisps in speech can prove to be of great difficulty to many, as they may be unaware of whether they are lisping. To help those affected, we have developed a simple algorithm for the real-time identification of the sigmatismus lateralis in ”S” sounds within speech via analysis in the frequency domain. The algorithm identifies peaks within the lisp’s frequency band after calibration. A frequency band of 3000-4000 Hz has been identified to be generally accurate for lisp and 2500-3000 Hz for the correct pronunciation for a single male test subject. The algorithm splits given speech recordings into smaller segments and compares the number of lisps and non-lisps detected in these segments to categorize. From tests, it was concluded that a segment length of 0.5 s produces the best results. The algorithm does not detect every lisp section, however it does not raise false positives. Our implementation in Julia with multi-threaded per-file analysis is able to analyze 20 files of lengths between 5 s and 10 s within 0.21 s on a Qualcomm Snapdragon 860 smartphone chipset, meaning analysis is far faster than real-time. The proposed algorithm is a simple prototype algorithm capable of realtime analysis of audio in the frequency domain to identify whether lateral lisps are the dominant sibilant pronunciation in a given window. The method was only tested for a single test subject. However, a calibration algorithm capable of adjusting parameters to new individuals is proposed. The algorithm itself should be easily expandable to identify other speech impediments.

The medical field has made significant improvements over the past decades, leading to an older and healthier population. One of the main core problems being tackled is the detection of falling events. The quick detection of a fall contributes greatly to the fast arrival of help and prevents further damages. Therefore an intelligent crutch was developed with the purpose of detecting a fall and the following call for help if needed, as well as conveying the location of the user to relatives and care givers. The crutch was evaluated in a series of experiments during which all falls were identified correctly and alerting was initiated.

Positron emission tomography (PET) is a widely used imaging modality in nuclear medicine for a variety of applications. Amongst the methods used for the quantifying and interpretation of PET images, the standardized uptake value (SUV) is a widely-adopted semi-quantitative tool that supplements visual understanding with quantitative information. SUV is used in both clinical and preclinical practices to report the status of various normal organs and tumors under investigation using PET imaging. While the determination of SUVs is typically done manually, which can be tedious, artificial intelligence (AI) can be utilized to enhance the efficiency of the process. In this study, a U-Net-based approach was employed for semi-automated determination of SUV in FDG-PET scans of mice brains. First, a U-Net model was trained using 50 FDG-PET images of six mice to perform the automatic segmentation task. The trained model then delineated the brain of a mouse which was then processed by a short in-house code to extract data and calculate the SUV. The process was also replicated in a manual way for comparison purposes. The comparison of the results from the U-Net-based segmentation method and the conventional manual method at nine different time points revealed that there were errors of less than 4.5% in eight out of the nine-time points. Although our U-Net model’s performance needs improvement, adapting a well-trained AIbased approach for SUV determination, particularly in preclinical studies, can help reduce the workload of organ delineation and minimize associated errors, facilitating SUV determination.

To ensure accurate and consistent imaging of patients, medical imaging systems are controlled and tested using phantoms. Despite the availability of commercial standard phantoms for decades, 3D printing technology has gained special attention as a tool for producing accurate and costeffective tissue-mimicking phantoms. As artificial intelligence (AI) becomes increasingly prevalent in medical imaging, dedicated phantoms are needed for testing their reliability, robustness, and quality before they are implemented in clinical settings. In this context, 3D-printed imaging phantoms, which have specific requirements relevant to AI models, can play a crucial role. Due to its unique ability to create phantoms of almost any complexity, 3D printing technology seems a suitable approach for the quality control of AI models in medical imaging. The following reviews some of the works that used 3D-printed technology to create custom-built phantoms for use in Computed Tomography (CT), nuclear imaging, Magnetic Resonance Imaging (MRI), and ultrasound imaging. The focus of this short review is on the accuracy of 3D-printed technology in creating imaging phantoms. In the end, the potential of the 3D-printed phantoms in testing and quality control of AI-based algorithms in radiology is discussed.

An intraoperative probe is a small tool used to identify, locate, and dissect pathological lesions during surgery and confirm their removal. The most common type of probe is the gamma probe, which detects gamma-emitting sources. Despite the successful use of gamma probes in clinics, their application can be limited when the background radiation from healthy tissue nearby or the injection site is high. In these cases, the use of beta probes that detect beta-emitting radiotracers would be beneficial. The shorter range of beta radiation compared to gamma radiation would result in improved detection efficacy when background radiation is significant. In this simulation study, we examined how geometrical parameters may impact the performance of a beta probe. Our results showed that the source depth inside the phantom and detector size affected the performance of the beta probe. We also proposed and evaluated two designs of beta probes capable of adjusting their performance.

Patients and caregivers suffer from alarm-related noise in today’s hospitals. While this is a topic for all fields of care, this challenge is especially relevant for intensive care units (ICUs). The noise harms patients, hinders the recovery process, or can even lead to psychosis. For the caregivers, noise causes an enormous stress level and in combination with the high number of clinically irrelevant or false alarms it leads to alarm fatigue and desensitization. These problems result in documented deaths of patients. Therefore, we present a demonstrator of a Silent ICU. Based on a distributed alarm system, the medical devices at the bedside do not create any audio alarm signals, as long as a remote alarm notification system is connected and works properly. An ICU bed place consists of devices from different manufacturers. Thus, a standardized interconnection is necessary. Therefore, we use IEEE 11073 Service-oriented Device Connectivity (SDC) to create a cross-manufacturer distributed alarm system with components from three different companies. A fourth company provides a documentation system for this demonstrator. Patients and caregivers will greatly benefit from Silent ICUs and smart distributed alarm systems that will be build based on interoperable cross-manufacturer environments.

Ventilator-associated pneumoniae (VAP) are a major problem in intensive care units. Previous in vitro experiments revealed that blue or violet luminescent endotracheal tubes are capable of inhibiting bacterial growth and may thus prevent pathogens from entering the lung. However, while these in vitro studies were conducted in a relatively transparent bacterial suspension, subglottic secretions around endotracheal tubes can also contain highly absorbent components, such as blood. To investigate if light has an antimicrobial effect under such conditions, staphylococcal solutions containing various absorbent components were irradiated by blue or violet luminescent endotracheal tubes for up to 24 h in a tracheal model. Light was generated externally by LEDs or lasers and entered the tube via light guides. An antimicrobial effect was observed for blue and violet light, which was, however, inhibited to some extent in the presence of light adsorbing molecules. Under these conditions, violet light had a stronger effect than blue at low absorptions, while at strong absorptions the effects converged, with blue light even exhibiting a slightly stronger impact. Significant differences between lasers and LEDs could not be detected. In our model, inhibition of bacterial growth could be observed even in the presence of light absorbing molecules. Illuminating endotracheal tubes with blue or violet light may thus represent a promising strategy to migration of bacteria from the oropharynx into the trachea and, thereby, possibly decrease the incidence of VAP.

Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. Different spectral setups have been introduced in the last years to allow intraoperative image-guidance. All systems suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low resolution. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method allows for continuous hyperspectral imaging of the surgical scene, while also enabling 3D scene reconstruction. The study demonstrates the feasibility of this approach and its potential applications in real-time monitoring and tissue identification of surgical interventions. The paper also discusses the technical challenges and future directions for improving the imaging system.

Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase recognition is the availability of training data and the large (visual) variability in procedures of different surgeons. Hence, a network must be able to generalize to new data. In this paper, we present an adaptation of a Temporal Convolutional Network for surgical phase recognition in order to ensure the generalization of the network to new scenes with different conditions on the example of cholecystectomy. We used publicly available datasets of 104 surgeries from four different centers for training. The results showed that the network was able to generalize to new scenes and we obtained recognition results with accuracy up to 82% on our own six captured surgeries, performed in a different hospital. This performance is similar for test data from the hospitals of the training data, suggesting that the network can well generalize to new surgical rooms and surgeons. The findings have important implications for the development of automated surgical decision support systems that can be applied in a variety of real-world surgical settings.

At ultra-high field MRI (Bo>7T) it is crucial to predict and control the patient safety. Commonly patient safety is controlled by the power deposited in the tissue (specific absorption rate - SAR). However, temperature distributions do not always correlate directly with SAR distributions, which makes temperature control also a crucial parameter to guarantee patient safety. In this work, temperature changes were accessed by MR thermometry, specifically by the proton resonance frequency shift technique (PRF). A phantom mimicking muscle tissue was used to evaluate the temperature rise caused by the radiofrequency (RF) absorption during 7T MRI, applied through a commercial birdcage head coil. A pulse-sequence protocol was implemented for both, the generation of temperature increase and the MR thermometry. To control the temperature, a digital thermometer was used, and oil tubes were utilized to dismiss the drift effects for PRF. Measurements of the phantom’s dielectric characteristics, i.e. conductivity and permittivity, were in good agreement with the literature values for muscle. Spatio-temporal evaluations showed a temperature increase in time via RF exposure and the feasibility of measuring temperature maps using the PRF shift method. The accuracy of the PRF shift method increased when the drift effects were quantified and dismissed, indicating a PRF reading accuracy differing less than 0.5 °C from the thermometer. Results also validate our heating and temperature imaging protocol. This study is a valuable contribution to the evaluation of heating effects caused by RF absorption and demonstrates potential impact on future thermal investigations, which may use different heating sources, as well validate thermal simulations.

Research showed that the normothermic machine perfusion of kidneys can enable prolonged storage and improve conditions compared to traditional cold storage. For research in this area, there is a demand for a long-term in vitro perfusion setup. In this work, we present a fully automated normothermic machine perfusion (NMP) system as an experimental research platform. The perfusion system is intended as a tool for researching the effects of different perfusion strategies on the kidney. To enable the automation, the NMP system consists of a blood pressure control, a circulation volume level control and a pH-regulation component. The setup is realized as a medical cyber-physical system consisting of networked embedded microcontroller nodes.

Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.

Inkjet printing is a versatile tool for the precise positioning of droplets that is used in many application areas such as 3D printing, biotechnology or pharmacy. This work focused on comparing the inkjet printability of different drug solutions of celecoxib (CLX), a non-steroidal anti-inflammatory drug, to produce drug depots in medical implants. CLX was dissolved in dimethyl sulfoxide (DMSO), poly(ethylene glycol) diacrylate (PEGDA) and a mixture of DMSO and PEGDA in a ratio of 1:1. The pure solvents and the drug solutions were inkjet printed using a Nanoplotter 2.1 with the NanoTip J printhead (GeSiM mbH, Radeberg, Germany). The voltage was varied from 60 V to 150 V using a step size of 5 V. Droplet volume and the trajectory of the droplets were investigated. For DMSO, DMSO-CLX, DMSO-PEGDA and DMSOPEGDA- CLX, reproducible droplet formation occurred with a droplet volume of approximately 300 pl - 500 pl and not more than one satellite droplet in a voltage range between 60 V to 80 V. Inkjet printing of PEGDA and PEGDA-CLX was not reproducible in the range of 60 V to 150 V. DMSO, DMSOPEGDA, DMSO-CLX and DMSO-PEGDA-CLX showed a high inkjet printability. In contrast, inkjet printability of PEGDA and PEGDA-CLX was very limited.

U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. Hence, we evaluate both approaches on a publicly available biomedical dataset. We find that Mask RCNN outperforms U-Net in segmenting overlapping cells and achieves comparable performance if they do not intersect. Our study provides valuable decision support to practitioners in selecting an appropriate method when solving instance segmentation tasks using deep learning, as well as important insights into enhancing the accuracy of such approaches in biomedical image analysis.

The documentation landscape for nursing care data in Germany is predominantly heterogeneous and unstructured. Therefore, insightful methods such as Artificial Intelligence (AI) are difficult to implement. We propose a stepwiseapproach that identifies relevant information from nursing theory and practice and maps it to a standardized nursing core data set to enable data-based improvement of nursing care. This can be used for various use-cases in care, such as risk detection and prevention in diverse care contexts. Many care processes can benefit of a cross-facility and standardized repository and associated applications. We propose an approach that enables the use of AI while leveraging consensus and evidenced-based expert knowledge from nursing science.

Running is one of the most popular sports worldwide motivating many non-athletic people to start new physical activities. But especially novice runners are at risk to develop injuries as they tend to run with prejudicial posture. Hence, there has been considerable research to identify factors improving running performance. One of these factors is the correct movement of arms as a swinging pendulum during running. Based on this knowledge ergonomically shaped handgrip elements were designed to improve the posture of the shoulder-arm complex during running. The main objective of this study was the validation of the actual effectiveness on shoulder-arm posture of these handgrips. In this sense 25 adult, healthy persons without running experience were examined by motion capture analysis during running on a treadmill with and without the use of the handgrips. The focus of movement analysis was on changes of hand supination, shoulder abduction and shoulder rotation. Flexion of elbows and shoulders were not expected to change and thus, were investigated as an internal control. Significant changes were found in sense of a reduction of forearm pronation, shoulder abduction, and internal rotation of the shoulder, resulting in improved posture during running. At the same time, no significant changes were found in elbow flexion and shoulder flexion. Future studies are needed to investigate the effects of the detected improvement of arm posture on other important running parameters, such as trunk stability, movement of lower limbs, running economy, and risk of injury. These studies should also compare effects in competitive athletes, recreational runners, and novice runners.

Image-guided and minimally invasive procedures still require confirmation on having reached a target. Intraoperative imaging is not always sufficient or conclusive as it comes with artifacts that can come with a certain amount of ambiguity and inaccurate location information. As an alternative to imaging, we want to explore sounds produced by the biopsy needle tip while advancing and interacting with tissue. In this paper, we show that by analyzing vibroacoustic signals acquired at the proximal end of the needle we are able to differentiate the tissue type. In total, 419 audio samples of 5 tissues were acquired and converted to spectrograms used as input to a convolutional neural network. Using this experimental setup we were able to differentiate the tissue types with an F1 score of 71.64%. Based on these results we were able to demonstrate the feasibility of our approach, as well as the importance of further experiments to ensure that vibroacoustic sounds produced by the needle tip can be a new navigation method.

Assessment of skin perfusion can reveal signs of deterioration and help to prevent critical states. Commonly applied clinical tests to capture skin perfusion are subjective and often hard to quantify. Laser speckle contrast analysis (LASCA) is a technique that can capture skin perfusion at high spatial and temporal resolution. LASCA requires, however, a complex setting and is not suited for monitoring under clinical conditions. Imaging photoplethysmography (iPPG) might be an easy to use alternative. However, direct comparisons of LASCA and iPPG, and thus proves of iPPG’s capabilities to capture skin microperfusion, are rare. In this work, we compare the longitudinal development of the amplitude of green channel and near-infrared iPPG and LASCA after application of a hyperemic test. Our results show statistically significant increases in amplitude over time in all modalities. The maximum increase in median normalized amplitude is 1.281 for the green channel, 0.594 for near-infrared and 1.111 for LASCA. Median Spearman’s rank correlation coefficient of the amplitude for green channel and LASCA is r= 0.89 and r= 0.71 for near-infrared and LASCA.

Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration process.We rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this.

Convolutional neural networks (CNNs) have proved their efficiency in performing image classification tasks, as they can automatically extract the image features and make the corresponding prediction. Meanwhile, the CNNs application is highly challenged by their vulnerability to adversarial samples. These samples are slightly different from the legitimate samples, but the CNN gives wrong classification. There are various ways to find the adversarial samples. The most common method is using backpropagation to generate gradients as the directed perturbation. Contrarily to set a constrained limitation, in this paper, we use iterative fast gradient sign method to generate adversarial images with the minimum perturbation. The CNNs were trained to perform surgical tool recognition as a configuration for the modern operation room. The coefficient or the learning rate which influenced the modification per iteration, was set to be adaptive instead of a fixed number. A few functions were utilized to perform the learning rate decay to compare the performance. Especially, we propose a new adaptive learning rate algorithm that consider the loss as a part of influence factor constitute the learning rate for the rest iterations. According to the experiments, our loss adaptive learning rate method was proved to be efficient to get the minimal perturbations for adversarial attack.

Time-domain measurement of the fluorescence lifetime in flow cytometry offers a complementary method to traditional flow cytometry, especially for the analysis of cells and biomolecules. Short measurement times due to high throughputs (cells/s) make a suited detector mandatory for a sufficient detection of the fluorescence signal and therefore the application of this method. We summarize the requirements for a suited detector for time resolved flow cytometry (TRFC) and present a new developed SPAD array detector with 100 pixels and 340 ns time between measurement windows, allowing a high-throughput of up to 60,000 cells/s. The functionality of the detector is shown with the measurement of a 22.2 ns laser pulse, making it applicable for an improved TRFC.

Electrical Impedance Tomography (EIT) has shown promising results as a low-cost imaging method for visualizing ventilation distribution within the lungs. However, clinical interpretation of EIT images is often hindered by blurred anatomical alignment and reconstruction artifacts. Integrating structural priors into the EIT reconstruction process has the potential to enhance the interpretability of the EIT images. Thus, a patient-specific structural prior mask is introduced in this contribution, which restricts the reconstruction of conductivity changes within the lung regions.We conducted numerical simulations on four finite element models representing four different thorax excursions to investigate the impact of the structural prior mask on EIT images. Simulations were performed under four different ventilation statuses. EIT images were reconstructed using the Gauss-Newton and discrete cosine transform-based EIT algorithms.We conducted a quantitative analysis using figures of merit to evaluate the images of the two reconstruction algorithms. The results show the structural prior mask preserves the morphological structures of the lungs and limits reconstruction artifacts.

There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the ‘.cnt’, eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction.

A profound understanding of brain disorders is essential for developing more effective treatments, targeted therapies, and reliable diagnoses. Electrical in vitro long-term and repetitive measurements give insight to neural activity of more complex neural circuits and, thus, into malfunctioning caused by illness. Flexible microelectrode arrays (flexMEAs) enable the recording of neural activity with promising spatial and temporal resolution over longer durations. The present study aims to combine the long-term recording by flexMEAs with the cultivation of brain slices inside an incubator. An 8 μm-thin flexMEA with up to 256 electrodes with 30 μm in diameter and interelectrode spacing of 200 μm was fabricated. Characterization showed uniform and reproducible electronic characteristics. Coating with PEDOT:PSS by electropolymerization formed stable low-impedance spectra. The platform is ready for future experiments with brain slices inside an incubator.

Various manufacturing techniques are available for polymer stent fabrication. Polymer semi-finished products can be prepared using solvent based methods, e.g. dip coating, as well as thermal processes, e.g. extrusion. These different methods may lead to an altered polymer crystal structure, resulting in a different deformation mechanism during mechanical stress. For the material property characterization needed for implant development, the test specimens usually are prepared using laser-cut or die-cut methods. Due to these different preparation protocols, a change in polymer microstructure, causing a material property variation, may also result. For this purpose, comparison of laser-cut versus die-cut of PLLA films has been performed. PLLA films have been prepared by dip-coating and were evaluated with respect to structural, thermal, and mechanical properties. In this study, a combination of uniaxial tensile tests, SEM and DSC studies was used. The results of the mechanical tests showed drastic differences in the elongations at break of die-cut specimens compared to laser-cut ones. The results point out formation of complex crystal structures during the manufacturing process. By the use of SEM imaging and DSC measurements, we were able to attribute these changes to the different plastic deformation mechanisms.

Mechanical characteristics of hard and soft tissues are central features for the quantitative description of tissue properties. In this study, we present a biphasic 3D-FE-based method to determine the biomechanical properties of sheep stifle joint articular cartilage (load-bearing area of the medial and lateral femur condyle, n = 28) from stress relaxation indentation tests (s = 0.1 mm, t = 180 s). The FE-model computation was optimized by exploiting the axial symmetry and mesh resolution. Parameters were determined using the Levenberg-Marquardt-algorithm. Results showed significant differences between the biomechanical parameters of the lateral (n = 11) and medial (n = 17) femur condyle of the sheep stifle joint articular cartilage. R² of the fit results varied between 0.91 and 0.99. Overall values for the Young’s modulus were 1.316 ± 0.778 MPa, for the Poisson’s ratio 0.106 ± 0.088 and for the permeability 0.008 ± 0.004 mm4/Ns. Future work will focus on studying the influence of experimental settings on the results obtained with the biphasic 3D-FE-model.

The accurate separation of cardiac and ventilatory contributions to electrical impedance tomography signals is crucial for complete and non-invasive cardiorespiratory monitoring. However, no consensus on a suitable source separation algorithm was achieved despite several proposals due to lacking systematic evaluation. To address this, we propose a benchmarking 4D finite element method generative model for mixed, cardiac, and ventilatory signals. Our model implements dynamic modelling of the heart, lungs, and pulmonary arteries using realistic volume and flow curve templates, along with cardiac and respiratory frequency coupling.We also employed variable alveolar and blood conductivities. The model was able to obtain long recordings faster than comparably complex models while maintaining significant physiological effects and signal properties such as non-stationarity, spatial delays, time and frequency profiles. The realistic physiological model can be used to taxonomize and evaluate source separation algorithms, as well as aid in the development and training of new ones.

Hospital-acquired infections occur through microbial contamination of the surgical wound and can lead to severe complications. A significant transmission path is the aerogenic transmission, where pathogens stick to floating particles like skin scales or to air moisture. A novel porous airflow ring which is placed around the surgical field aims to overcome this by applying sterile air directly at the operation wound. The ring is provided with an air tight coating at the outer side and allows for fixation on the skin by an adhesive coating at the lower side. To evaluate its performance the airflow ring was placed in an atmosphere with nebulized suspension of Staphylococcus arlettae of a concentration of 5.0 x 10^5 CFU/ml resp. 5.0 x 10^6 CFU/ml within a box. The formation of bacterial colonies (CFU) on contact plates placed within the airflow ring was subsequently determined by visual counting after incubating at 37 °C for one day. CFU counts of the ventilated and the unventilated situation were compared. With the smaller inoculum, the introduction of bacteria into the inner site of the ring was completely prevented, whereas the contact plate of the unventilated ring resulted in 77 to 427 colonies in different trials. With the higher inoculum, the bacteria ingress was very strongly reduced by 99.7% respectively 99.9%. In conclusion the airflow ring shows a strong shielding effect for germs adhered to fog-sized water droplets. To clearly demonstrate the effect, the number of bacteria was greatly increased compared to reality in this setup. It was shown that it can withstand even conditions significantly worse than those encountered in an operating theatre. In order to demonstrate the effect in vivo, clinical trials have to be conducted to confirm the laboratory results.

The use of 3D printing technology for medical applications is becoming increasingly popular. Recent stereolithography (SLA)-based printing methods allow the generation of complex structures with a high surface quality. This is particularly useful for applications in the neurovascular field, where sophisticated structures are involved. In this study patient-specific intracranial aneurysm models are extracted based on medical image data und printed as thin-walled vascular phantom models. For this purpose, two commercially available 3D printers are used to print flexible vascular models with three different silicone-like elastic resins (Ultracur3D FL 300, Prusament Flex80 and Formlabs Elastic 50A) of various Shore hardness. Three aneurysm models of different size and complexity are chosen. To evaluate the geometric accuracy of the flexible models, angiographic measurements are performed for an exemplary case and morphological parameters are extracted from the generated 3D models. The printed results demonstrate a successful generation of hollow aneurysm phantoms. There are dependencies regarding the print quality from the model to platform positioning for two materials. The quantitative geometric accuracy analysis shows notable differences between the materials. The extracted morphological parameter values for all materials show a mean decrease compared to the original reference model of aneurysm volume (4.5 %) and maximum diameter (1.0 %) as well as an increase of ostium area (6.0 %) and maximum height (4.9 %). However, Formlabs Elastic 50A in particular exhibits just slight reductions with respect to the reference model, with a mean decrease for all parameters of 5.7 % as well as no dependence on printing position and resulting artifacts. The study investigates the feasibility of using SLA-based 3D printing to generate realistic flexible aneurysm phantoms. In this context, the Formlabs Elastic 50A could be identified as potentially applicable for phantom creation in terms of reproducible quality and geometric validity.

Elevated intraocular pressure is the main risk factor for glaucoma and its adjustment to physiological levels can prevent progression of the disease. If conventional drop-based glaucoma therapy fails, surgical interventions such as trabeculectomy or the application of implants, which drain aqueous humour from the eye and thus reduce intraocular pressure, are used as final therapeutic procedures. These interventions are becoming increasingly minimally invasive and the drainage implants are getting steadily smaller, which is why these interventions are now called microinvasive glaucoma surgery (MIGS). Both, surgery and drainage implant-based glaucoma therapy are limited by wound healing processes and fibrosis that occlude the newly created drainage pathways for the aqueous humour. To suppress unwanted postoperative fibrosis coupled with an increased extracellular matrix production, cytostatics like mitomycin c or 5- fluorouracil are currently used, which are associated with side effects. Therefore, more specific antifibrotic agents are key for a durable functionality of the interventions in glaucoma therapy. In this study a specific active substance (pirfenidone, PFD) was added in a polymer matrix for controlled release. Previous studies have shown that the release of pirfenidone from a thermoplastic silicone polycarbonate elastomer, which could be used as a material for a glaucoma drainage implant, has been relatively fast. To decelerate the release the influence of the alternate polymers Poly-L-Lactide (PLLA) and Poly-D,LLactide (PDLLA) as alternative drug carriers was investigated. Furthermore, a reduction of the pirfenidone content was used to delay the release.

An unfulfilled pregnancy is often due to impaired patency of the female Fallopian tubes. To circumvent tubal obstruction, cost-intensive in vitro fertilisation (IVF) is currently indicated. The only therapeutic treatment concepts available are transvaginal tubal lavage/catheterization or laparoscopy with minimally invasive or complex surgical tubal reconstruction, which are associated with side effects. The development of new and cost-effective non- or minimally invasive therapeutic concepts that can restore the patency of the Fallopian tubes would be key in the treatment of female infertility and can fulfil the desire to have children. Therefore, in the present work, ex vivo anatomical studies are carried out on various laboratory mammals (pigs, rabbits, guinea pigs). Using these animal models, it was also evaluated whether a sensor-ureterorenoscope can be inserted through the whole female reproductive tract to the Fallopian tubes. This device will be used for endoscopically guided implantation of a novel tubal microstent prototype via its tailor-made delivery system into the isthmus region of the Fallopian tubes in upcoming studies. This work serves to identify an optimal species for a longitudinal animal study, in which the feasibility as well as the tissue reaction to the implant will be evaluated.

A diagnostic medical tool is described that allows patients to non-invasively observe, describe and document their own retinal blood vessels using the Purkinje vascular entoptic test. We envision key applications of the Purkinje retinal image stabilization (PRIS) device in monitoring of diabetic retinopathy and cost-effective assessment of retinal visual acuity. To optimize our PRIS principle, we have developed a benchtop device with interchangeable light modules. The modular design is intended to provide maximum flexibility for determining appropriate PRIS stimulation parameters. In self-tests, we found that a pleasant entoptic perception occurs when a green luminous light spot (approx. 525 nm) moves on a circular path. The challenging part is to derive an objective diagnosis from a subjective phenomenon.

In computed tomography (CT) materials with high-atomic number Z cause image artefacts, thus, errors in CT numbers given in Hounsfield Units (HU). Also, the conventional HU scale (CHU) implemented in CT scanners is truncated, i.e., it does not cover high-Z materials. These restrictions lead to incorrect mapping of CT numbers to electron density, which are used in radiotherapy (RT) treatment planning systems (TPS). Even analytical conversion methods are only permissible for tissue-equivalent materials. In terms of HU-to-density conversion in RT TPS, we investigated the CT numbers of material mixtures up to Z<29 at the CHU and an extended-HU (EHU) scale, respectively, and quantify the systematic errors of image artefacts. In [1] the feasibility of a stoichiometric analytical calibration method were analyzed for metals and adapted for higher accurcy, for energies of 80 kV and 120 kV. In this work, we add results for 100 kV and 140 kV to cover the wide diagnostic range. The CT numbers are effected by physical and machine-based properties and depend strongly on the energy, e.g., for Cu a HU difference of 6 171HU at 80 kV and 140 kV occured. The analytical calibration parameters change with energy by a factor between 2 and 10 depending on the physical process. Although for high- Z materials our calibration procedure remains in conflict with rigorous physics [2], it offers an improved and a practical way to predict electron densities from CT numbers.

The radial strength of balloon expandable stents represents a key property for a successful recanalization of sclerotic blood vessels. This study focuses on the most commonly used method for investigation of radial strength and radial stiffness via segmented head test setup. A custom made user software was used for evaluation of the radial force curves considering requirements of international standards such as ASTM F3067. Contributing factors during measurement such as friction and test setup deformation as well as the single cycle and multi cycle approach were addressed and discussed.

The use of intracortical electrodes is a crucial method for medical diagnosis and treatment and holds the promise for overcoming various neural diseases and challenges in prosthetics. The general physical and electrical function as well as the biocompatibility is extensively tested before intracortical electrodes are implanted, however, their longterm performance, is still limited through tissue scarring and electrode deterioration, and consequently a decrease in signal quality. To overcome this challenge, aspects such as materials selection, coatings, flexibility, and surgical procedures are often addressed. To date, however, a systematic, industry - compatible process for long-term functional testing of new intracortical electrodes technologies is still lacking. Implementation of a general overall in vivo testing procedure with defined quality criteria would enable reliable in vivo longterm (> 30 days) device assessment. This could reduce the risk in developing probes for long-term performance and pave the way for clinical application in patients suffering from neural diseases.

The glabellar tapping reflex (GTR) is a sign related to brain conditions and can be analyzed by clinicians for diagnostic purposes. To facilitate the quantitative analysis of this reflex, we developed a video-based tool using the MediaPipe framework. We tested our approach on healthy subjects to assess the effect of age and gender on reaction time and blinking duration. The reaction time results show that the young group has a mean value (±standard deviation) of 0.091 (±0.066) seconds and the old group has 0.085 (±0.052) seconds, while female and male subjects have 0.097 (±0.053) seconds and 0.080 (±0.064) seconds respectively. For blinking duration, males have a mean value of 0.216 (±0.077) seconds and, females have 0.189 (±0.115) seconds, while old and young groups have 0.132 (±0.039) seconds 0.267(±0.084) seconds respectively.

Uniaxial tensile tests are the most common method for investigating the mechanical properties of materials. However, due to the small strength and size of certain materials, larger conventional universal testing machines are sometimes not suitable for performing elongation testing, so dedicated test setups are required. Therefore, the authors developed a testing device to measure the mechanical properties of small, flexible materials, e.g. fragile specimens of the capsular bag of the eye. Also different variants for specimen holders were designed and tested. Initial tests using eggshell membranes as a model material demonstrated the functionality of the test setup at different strain rates.

The objective of this study was to assess and present technical data related to resuscitation procedures that involved the application of a defibrillator for at least one shock. Specifically, we sought to distinguish two rectangular defibrillation impulses in terms of varying phase durations. The evaluation encompassed a range of defibrillation metrics and metadata, including transthoracic impedance, mean and peak currents. The results of this analysis provide valuable insights into the technical aspects of defibrillation.

Implant-associated infections remain a challenge in biomedical engineering and prevention of such complications is a topical subject. For that, local drug delivery is associated with a number of advantages over systemic antibiosis. In this context, incorporation of antibiotic drugs into fibrous nonwovens is a promising approach for the generation of antiinfective implant envelopes or wrappings. Such materials can be manufactured in large scale and electrospinning process is suitable for a wide range of drugs to be incorporated. With regard to increasing numbers of pacemaker implantations and associated risk factors for infections, biodegradable ceftriaxone loaded poly(L-lactide)-co-poly(ethylene glycol) (PLLA-co-PEG) nonwovens have been characterised concerning the influence of the drug on morphology, thermal and mechanical properties and drug release behaviour. Our results show that the chosen material combination is promising for further approaches towards an antiinfective nonwoven pacemaker envelope.

We report the optimized wafer-scale fabrication of microporous membranes for applications in the biomedical field such as cell filtration. Existing similar devices can mostly not be integrated on CMOS circuits or mass fabricated. Both is enabled here by the exclusive use of scalable and low temperature microsystems technology methods on silicon wafers. The successfully manufactured devices are characterized with regard to the feasibility of an integrated clogging detection or captured cell counter. The results from electrochemical measurements across the chips match the calculations from a corresponding theoretical model well, verifying the described concept. Further electrical functionalities may thus be integrated into the micromembrane device in the future, equipping it for new applications and allowing a more efficient solution for existing tasks of similar devices.

Hand gesture recognition using data from electrical impedance tomography (EIT) systems offers many promising applications, for example in the field of human-computer interaction. Due to its real-time capability and the use of harmless currents for humans, it can be used in medicine, robotics, or virtual environments. As already shown in similar works, for example by Zhang et al. finding a good compromise between accuracy, precision, framerate and the size of the system is a challenge [1], [2]. This work presents a truly wearable compact EIT system on a single 24.9 mm × 22.5 mm circuit board consisting of standard components without application-specific integrated circuit. A neural network (NN) classifies two gestures. It is able to distinguish two different gestures with an accuracy of 78,33 % and a precision of 76,56 %. This work has a strong focus on the size of the system and provides a starting point for further research in compact wearable gesture recognition systems. It shows the challenge of the compromise between size and quality of the signals.

Cerebral aneurysms are common lesions in the general population and are increasingly treated by endovascular procedures. Training of these methods not only improves the learning period of physicians but also increases patient safety. However, the use of traditional medical training methods such as training in living animal models is decreasing due to high costs and ethical issues, so computer simulations and 3D-printed phantoms are being used instead. A more realistic simulation requires pulsatile blood flow that can be customized and has body temperature. Current simulators that use 3D-printed phantoms are mainly used under X-Ray in an angiography intervention room, therefore a dual-use simulator that is easy to transport and can be used also without X-Ray is desirable. Either it could be used traditionally in an angiography intervention room or externally in any regular room with an image simulation according to rotational angiography. In this work, a prototype for blood flow simulation is presented as a first step towards such simulator and its application is demonstrated through different investigations. The fluid system is capable of simulating a given blood flow profile and the heating system can generate an accurate body temperature. The case can be easily integrated into a modular phantom system and is convenient to transport, although the weight could be further reduced.

For the in vitro examination of the effects on cells and tissues of metal ions derived from implants by wear and corrosion, a system is needed to release metal ions in a controlled and sustained manner. As small concentrations may have an impact on biological systems, a corresponding slow release over a defined period of time is necessary. Here, a manufacturing method of polymeric Poly(L-lactic acid) (PLLA) films with incorporated cobalt ions via dip coating is reported. The establishment of two analytical methods for the detection and quantification of cobalt ions is shown. High performance liquid chromatography enables cobalt quantification over a concentration range of 0.33 μg/mL up to 33 μg/mL in sample solutions. Extraction and detection of cobalt ions from solvent cast PLLA-cobalt-films shows the feasibility of the manufacturing method. Initial biocompatibility tests with manufactured PLLA-cobaltfilms showed no effects on adipose derived primary mesenchymal stem cells (adMSCs).

Magnetic motion sensing enables non-contact tracking of relative position and orientation in 3D space. With recent advances in sensor and actuator devices, applications in human movement analysis seem feasible. However, establishing a setup from scratch in terms of hardware and software is challenging. Therefore, we introduce a comprehensive simulation pipeline based on a digital twin concept that enables the design and validation of new approaches based on kinematics, magnetics, and digital real-time signal processing. We also elaborate on related applications.

Dynamic mechanical properties of polymers for biomedical applications are crucial parameters for development and engineering of new medical devices. Here, the time-dependent material behavior is a key factor for durability. Varying the strain rate is a convenient implementation of time-dependency for uniaxial testing. This study investigates time-dependence of Poly(L-Lactide) (PLLA) through uniaxial testing with different strain rates and PLLA with different molecular weight. The results show strain dependence for elongation at break and yield stress, Young’s modulus however is not rate dependent. An increase in elongation at break is also seen with increasing molecular weight of PLLA. Plastic strain increases significantly only for PLLA with an intermediate inherent viscosity. Results show distinct time dependencies regarding strain rate for PLLA with slightly different inherent viscosities. For stent-related mechanical material characteristics, higher molecular weight PLLA seems to be advantageous. This study only considers base materials, although appropriate thermal, mechanical as well as chemical post processing approaches for further adjustment of different properties have already been shown. A combination of the best possible base material and a suitable post-processing should be targeted.

Automatic generation of intraoperative anaesthesia reports has great potential to generate accurate and efficient documentation and allow the anaesthesiologists to utilise their time more optimally. However, automatically generating anaesthesia reports represents a demanding component in future operating rooms. In this paper, a computer-assisted system for anaesthesiology report generation during laparoscopic surgery is proposed. Data from anaesthesia machine, patient monitor, and surgical devices were acquired and analysed. The system proved to generate more accurate, efficient, and better readable reports compared to hand-written ones by clinicians. Moreover, additional information that describes surgical activities and events was also added by analysing surgical data.

Supervised Neural Networks are used for segmentation in many biological and biomedical applications. To omit the time-consuming and tiring process of manual labeling, unsupervised Generative Adversarial Networks (GANs) can be used to synthesize labeled data. However, the training of GANs requires extensive computation and is often unstable. Due to the lack of established stopping criteria, GANs are usually trained multiple times for a heuristically fixed number of epochs. Early stopping and epoch selection can lead to better synthetic datasets resulting in higher downstream segmentation quality on biological or medical data. This article examines whether the Frechet Inception Distance (FID), the Kernel Inception Distance (KID), or the WeightWatcher tool can be used for early stopping or epoch selection of unsupervised GANs. The experiments show that the last trained GAN epoch is not necessarily the best one to synthesize downstream segmentation data. On complex datasets, FID and KID correlate with the downstream segmentation quality, and both can be used for epoch selection.

This paper presents a software-based Python framework for developing future AI-enhanced end-to-end Brain-Computer-Interfaces (BCI). This framework contains modules from the emulated analogue front-end and from neural signal pre-processing for invasive neural applications. These modules can be assembled into several pipeline versions for evaluation and benchmarking. The aim of this framework is to accelerate the development of BCIs due to system-wide optimizations in order to set the requirements for hardware development without prior knowledge on the basis of accuracy (recall and precision) and latency. In the next step, the pipeline can be optimised for on-chip and embedded execution.

Implantation of a shunt system is the most common neurosurgical procedure for the treatment of hydrocephalus. Hydrodynamic parameters of hydrocephalus shunt systems are valuable variables to address patients' needs. In this report, we present a test setup to evaluate hydrodynamic parameters of hydrocephalus shunt systems. The test setup was validated using a stainless steel capillary and compared with the analytical solution according to Bernoulli's equation. It was demonstrated that the experimental setup is able to model the pressure in a physiologically relevant range. The measured and averaged flow resistance was 2.96 mmHg/(ml min-1). According to the analytical solution of Bernoulli's equation, the flow resistance is 2.86 mmHg/(ml min-1). Therefore, the measured flow resistance is 3.5% higher than the analytical solution. Moreover, the nonlinear characteristic of the pressure drop at the inlet and outlet of the capillary plays a minor role compared to the friction of the tube flow. As a result, the increase in flow rate with increasing pressure load can be well approximated by a linear function for the low flow rates measured here. The experimental setup presented will be used in the future to characterize commercially available shunt systems under various hydrodynamic conditions.

Many researchers have postulated inexpensive methods to estimate respiratory volumes from respirationinduced movements of the upper body. In the development of smart garments, the number and the optimal positioning of sensors is an essential aspect. Therefore, the symmetry of upper body respiratory movements was investigated based on the measurement data from a motion capture system. In the ventral central area, coefficients of determination above 0.99 regarding movements of symmetrical markers mean that sensors can be placed without restrictions in the position mirrored on the sagittal plane. This simplifies sensor positioning in a smart shirt and expands the range of applications of smart shirts for respiratory monitoring.

Atrial fibrillation (AF) is the most common sustained arrhythmia posing a significant burden to patients and leading to an increased risk of stroke and heart failure. Additional ablation of areas of arrhythmogenic substrate in the atrial body detected by either late gadolinium enhancement magnetic resonance imaging (LGE-MRI) or electroanatomical mapping (EAM) may increase the success rate of restoring and maintaining sinus rhythm compared to the standard treatment procedure of pulmonary vein isolation (PVI). To evaluate if LGE-MRI and EAM identify equivalent substrate as potential ablation targets, we divided the left atrium (LA) into six clinically important regions in ten patients. Then, we computed the correlation between both modalities by analyzing the regional extents of identified pathological tissue. In this regional analysis, we observed no correlation between late gadolinium enhancement (LGE) and low voltage areas (LVA), neither in any region nor with regard to the entire atrial surface (-0.3<r<0.3). Instead, the regional extents identified as pathological tissue varied significantly between both modalities. An increased extent of LVA compared to LGE was observed in the septal wall of the LA (a sept ., LVA = 19.63% and a sept ., LGE = 3.94%, with = median of the extent of pathological tissue in the corresponding region). In contrast, in the inferior and lateral wall, the extent of LGE was higher than the extent of LVA for most geometries (a inf ., LGE = 27.22% and a lat ., LGE = 32.70% compared to ainf., LVA = 9.21% and a lat ., LVA = 6.69%). Since both modalities provided discrepant results regarding the detection of arrhythmogenic substrate using clinically established thresholds, further investigations regarding their constraints need to be performed in order to use these modalities for patient stratification and treatment planning.

In spite of advances in the treatment of cardiovascular disease, there remains a significant need for high-quality evidence for clinical practice given the rising prevalence worldwide. Next to the increase of regulatory requirements, poor screening-to-recruitment ratios, lack of patient engagement, and long timelines for contracting and institutional review board (IRB) approval are leading to an outflow of clinical trials in the EU. Patient registries are a potentially rich source of data, especially for evaluating the course of rare diseases and effects of new treatments. In the sectors of regulatory assessments of medical devices they are greatly underused. Health-related real world data provide crucial support for regulatory decision-making, particularly post-market assessments of medical products. Registries can be used not only for observational studies, but also for conducting experimental study designs. Register-based randomized controlled trials (RRCTs) are of particular importance here, as they can combine the advantages of randomized controlled trials (RCT) and registers. The government's financial support and reduction of regulatory barriers to the implementation of registry-based RCT are incentive factors for manufacturers to let their medical devices (re-)certified. Furthermore, it is essential that the necessary criteria are already taken into account when setting up the German implant registry in order to enable future RRCTs. The presented work is an additional pleading for the use and extension of registries for all medical products.

Few-shot learning addresses the problem of classification when little data or few labels are available. This is especially relevant in histopathology, where labeling must be carried out by highly trained medical experts. Prototypical Networks promise transferability to new domains by using a pre-trained encoder and classifying by way of a prototypical representation of each class learned with few samples. We examine the applicability of this approach by attempting domain transfer from colon tissue (for training the encoder) to urothelial tissue. Furthermore, we address the problems arising from representing a class via a small amount of representatives (prototypes) by testing two different prototype calculation strategies. We compare the original “Prototype per Class” (PPC) approach to our “Prototype per Annotation” (PPA) method, which calculates one prototype for each example annotation made by the pathologist. We test the domain transfer capability of our approach on a dataset of 55 whole slide images (WSIs) containing six subtypes of urothelial carcinoma in two granularities: “Superclasses”, which combines the tumorous subtypes into a single “tumor” class on top of a aggregated “healthy” and additional “necrosis” class, and “subtypes”, which considers all eleven classes separately. We evaluate the classic PPC approach as well as our PPA approach on this data set. Our results show that the adaptation of the Prototypical Network from colon tissue to urothelial tissue was successful, yielding an F1 score of 0.91 for the “superclasses”. Furthermore, the PPA approach performs very comparably to the PPC strategy. This makes it a viable alternative that places more value on the intent of the pathologist during annotation.

Degradable polymeric Biomaterials, such as polylactic acid, are widely used in medical field as bioresorbable implant coatings. More recently, stent covers, implantable tubes or wound dressings made from electrospun nanofibers have been developed. In order to tailor the material for specific applications, dedicated fine-tuning procedures are required. To modify fiber interconnection or porosity, solvents or their dilutions are applicable. In this work, the effect of differently diluted protic and aprotic-polar solvents on electrospun poly-L-lactic acid (PLLA) nonwovens was investigated via immersion or vapor exposure. Fiber adhesion was observed with protic solvents. Fiber destruction and increased polymer crystallinity was observed with aprotic polar solvents. The immersion duration revealed to be far more irrelevant than solvent dilution. None of the tested solvents affected molecular weight. This opens up significant research potential to replace halogenated solvents as a cornerstone of eco-friendly “green” electrospinning processes. The results of this study are suggesting that biomaterials with desired properties can be produced in a targeted manner.

The promising results of transcatheter aortic valve replacement (TAVR) over the past two decades indicate an expansion of the patient cohort toward patients with intermediate or low surgical risk. Since some complications of TAVR have already been minimized, subclinical leaflet thrombosis (SLT) has gained importance in recent years. SLT is manifested by a thrombotic layer on the prosthetic leaflets that gradually reduces leaflet motion. The resulting decrease in functionality of the TAVR causes a need for re-intervention. The origin of SLT and approaches to prevent SLT are still unexplored. For this reason, we have developed an in silico model that can be used during the design development process of TAVR devices to estimate the thrombosis risk of the implant. Based on passive scalar transport, hemodynamic metrics are used to quantify platelet activation and aggregation which are associated with the formation of thrombosis. In conjunction with a numerical simulation model considering the fluid-structure interaction between the blood mimicking fluid and the TAVR implanted in an aortic root, the thrombosis risk can be modeled. The simulation model can be used to calculate the three-dimensional flow structures within the native sinus and neo-sinus and also provides the ability to derive metrics to assess the risk of thrombosis. We demonstrated that this in silico model is a time-effective tool to assess thrombosis risk in TAVR product development.

Severe aortic valve stenosis has been successfully treated with minimally invasive transcatheter aortic valve replacement (TAVR) for more than 20 years. The therapy is applied to an increasing cohort of patients. Nevertheless, paravalvular leakage (PVL) remains one of the main complications after TAVR implantation reducing safety and efficacy of the implant. To improve TAVR performance, virtual patient cohorts can be used to generate a significant amount of data with biologically varying conditions comparable to clinically realworld data. For this purpose, in silico models need to be established which are used to evaluate the possible occurrence of PVL. When establishing in silico models, the question between simplification to reduce computational effort and still valid information from the model must always be clarified. Thus, we investigated the comparability between steadystate and transient PVL measurements as an input parameter for simulations in virtual cohorts. A clinically established TAVR was implanted into different annulus models and tested in a steady-state back-flow test bench and in a pulse duplicator system. As a result, leakage rates were compared and assumptions for the in silico models were derived. Although the trend of PVL is comparable in steady-state and transient conditions, absolute values differ making it difficult to extract generalized assumptions. Especially when PVL increased, e.g. in a larger implantation diameter or in an imperfect annulus, the variation also increased up to 50% of the measured value.

The possibility of establishing a novel technique for reliably accessing glycemic information in a non-invasive, easy to implement method at THz frequencies via the fingernail bed is investigated. The nail bed’s major content is blood at its various glucose levels and is also partially protected from environmental conditions by the nail plate, making it a desirable platform for non-invasive glucose sensing. The study is based on a 2D computational electromagnetics (EM) model of the layered fingernail in COMSOL Multiphysics, where the required dielectric function (i.e. permittivity and conductivity) of the fingernail plate is measured using THz-Time-Domain- Spectroscopy (THz-TDS), and the glucose-dependend dielectric functions of the fingernail bed are taken from available experimental data in the literature. From this data a material model using a multipole Cole-Cole model is established for both, the nail plate and the nail bed, where the glucose content in the latter is varied from 3.0-19.0 mmol/l. A numerical analysis of the THz reflectometry at the fingernail in the frequency range of 0.1-2.0 THz revealed that the reflectance spectra are sensitive to the glucose content in the nail bed proving that accurate glucose sensing via the fingernail bed may become feasible around 0.2-0.4 THz.

Vascular diseases like aneurysms or atherosclerosis belong to the leading causes of death. For diagnosis and treatment, medical imaging is essential. Standard imaging methods are Ultrasound, X-ray-based Angiography, Computed tomography or Magnet Resonant Imaging. Additionally, Intravascular Ultrasound or Optical Coherence Tomography are available for intravascular imaging. One imaging technique for direct investigation of the inner vessel is vascular endoscopy, also called angioscopy. It is based on the insertion of an optical endoscope into the blood vessel for direct imaging. Dependent on the vessel diameter, these endoscopes have to be very small. Angioscopy can provide valuable information about the condition of the vessel wall and serve for procedure observation or implant assessment. But angioscopy requires the absence of blood for a clear field of view. In prior research, we have already developed methods for the placement of a tiny optical fiber inside a catheter combination. As an essential step toward a clinical study, we now want to evaluate the flushing parameters needed to achieve a clear field of view for the endoscope. The parameters are affected by the blood flow and vessel diameter. We designed a test setup that allows angioscopic examination and the simulation of realistic conditions, such as blood flow and different vessel diameters and that includes a test pattern for image quality assessment.

Magnetic nanoparticles are used in a wide range of medical applications. However, the accurate steering of the particles through the vascular system is still an open research question. Thus, this paper introduces a novel hybrid magnetic array for steering magnetic nanoparticles through a bifurcation. The array consists of permanent plus electromagnets and is based on a Halbach structure. It combines the advantages of the strong magnetic field of low-cost permanent magnets with the adjustability of electromagnets. The array is investigated numerically in a 3D model using COMSOL Multiphysics. In the simulations, the propagation of the particles in a symmetrical bifurcation is analyzed for discrete coil currents in the range of −10A to 10 A. The results reveal that for low negative currents of −1A to −0.5 A, the influence of the magnetic field of the permanent magnets can be almost completely eliminated, and only 0.5% and 0.9 %, respectively, of the particles were trapped by the magnets. Thus, the hybrid array outperforms the reference of a Halbach array fully built of permanent magnets. Overall, the proposed hybrid array is much more flexible, and the magnetic force can be adjusted more precisely compared to an array of permanent magnets.

Biofluid mechanics play an important role in the study of the mechanism of cardiovascular diseases and in the development of new implants. For the assessment of hydrodynamic parameters, experimental methods as well as in-silico approaches can be used, such as particle image velocimetry (PIV) and Deep Learning, respectively. Challenges for PIV are the optical access to the region of interest, and time consumption for measuring and post-processing analysis in particular for three dimensional flow. To overcome these limitations state-of-the-art deep learning algorithms could be utilized to augment spatially coarse resolved flow fields. In this study, we demonstrate the use of Physics Informed Neural Networks (PINN) to augment PIV measurement data. To demonstrate a combined workflow, we investigate the flow of a Newtonian fluid through a simplified aneurysm under laminar conditions. Generation of synthetic PIV particle images of a single measurement plane and the corresponding PIV vector calculations were performed as the basis for the PINN algorithm. Based on the Navier-Stokes equations the PINN reconstructs the entire 3D flow field and pressure distribution inside the aneurysm. We observed qualitative agreements between ground through data and PINN predictions. Nevertheless, there are substantial differences in the quantitative, locally resolved comparison of the flow metrics, despite the generally tendency for the PINN algorithm to correctly augment the flow field.

Regulation compliance, privacy, and security issues all need to be taken into consideration while using remote patient monitoring. This article on current developments in remote healthcare and monitoring sheds light on contactless techniques and draws attention to the difficulties still facing the deployment of remote patient monitoring. It is summarized how remote patient monitoring is already being used, paying special attention to Germany and its regulatory framework.

Dizziness is a common symptom in medicine. The anamnesis and detection of a nystagmus is essential to distinguish a vertigo's pathogenesis. The diagnosis is complex, expensive, and not always available across the board. We present a novel location- and time-independent mobile application for videonystagmography (VNG) to support vertigo patients and medical staff. No additional hardware is necessary. The app uses artificial intelligence for eye tracking and to detect a horizontal nystagmus. A feasibility study of the mobile VNG with 13 healthy volunteers was performed. Each participant underwent a caloric vestibular testing to provoke the presence of a vestibular nystagmus. It could be shown that a smartphone-based VNG is possible.

Wound healing can be delayed if the biomechanical stability of the wound closure is inadequate. Therefore, it is necessary to investigate different suturing techniques for their biomechanical stability. In this study, suturing techniques suitable for the forehead area were investigated. For this application, a special test setup was developed to simulate the curvature of the forehead and the corresponding physiological configuration. The average forehead curvature is 62.24 ± 4.11 mm in radius. To simulate this curvature, the skin specimens are subjected to tensile stress over the spherical surface using a standard uniaxial testing machine. For the evaluation, an automated evaluation tool for MATLAB was also developed. Three different suturing techniques (Straight, Lazy-S, Zigzag) were investigated and tested for their biomechanical stability. Of the three suturing techniques, the Zigzag suture proved to be the most stable with the highest stiffness of 44.23 ± 8.18 % and the highest final failure of 32.60 ± 4.95 % (relative to the control sample without incision). The study has shown that the test setup can be used to investigate different forehead suture techniques.

Bioimpedance spectroscopy can be used to investigate the composition and monitor the human body, organs, tissues, or cell cultures by measuring the voltage developed by the injection of small alternating currents at different frequencies. These currents are injected sequentially through a frequency sweep and a with a Howland current source or one of its modifications. However, the frequency sweep is not time efficient and introduces problems with data coherence in the case of bioinstability. On the other hand, the Howland current source requires high precision matching between its components. In this contribution we developed a custom-made device for bioimpedance measurements based on a multisine current waveform and on a negative-feedback topology for the current source. Measurements on passive elements showed that the device had less than 1Ω and 0.05∘ uncertainty in the frequency range between 500 Hz and 200 kHz for impedance between 1 kΩ and 10 kΩ. The measurements were affected by an inductive artifact connected with the limited common-mode rejection at high frequencies. Nevertheless, we could characterize the artifacts through a fitting procedure to recover the expected value of the targeted impedance.

Physical vapor deposition (PVD) sputtering is a promising technique for the generation of electrically conductive coatings on complex structured biomaterials, such as nonwovens, that can be used in implants. The ultra-thin coatings can fulfil a variety of purposes, such as drug depot or biosensor. The aim of our studies was to determine the feasibility of generating thin silver (Ag) coatings on complex structured poly-L-lactide (PLLA) nonwovens by sputtering and their morphological characterization. PVD sputter process was performed using a customized plasma chamber and a complex rotation of the tender electrospun PLLA nonwovens. Morphological analysis revealed a promising homogenous Ag coating, representing the structure of the nonwovens.

Introduction: Application-related (hands-on) training and instruction is an essential component in the advanced education of physicians in residency. To teach and train required endoscopic examinations and interventions in a standardized manner, courses are offered by various societies. Within these courses the assistant endoscopists learn and train how to use the technical equipment and how to perform the examinations and related interventions. Similarly, for graduate biomedical engineering students with a job- or research-related interest in the field of endoand laparoscopy, an early involvement and practical introduction to the instrumentation and techniques - if possible already during their studies - can generate a fundamental understanding of the technical requirements as well needs of the physicians. Objective: As various such (hands-on) training-courses for flexible and rigid endoscopy specifically addressing young physicians, surgeons as well as engineers are offered by numerous institutes, this contribution tries do provide a structured overview on/over these possibilities. Material: Known cross-institutional courses in the field of endo- and laparoscopy currently offered in Germany in cooperation of physicians and engineers are listed and briefly described. Results: A total of n = 4 crossinstitutional courses for the introduction and training in flexible and rigid endoscopy have been identified. Discussion: The cross-institutional courses for physicians and technicians in the field of endo- and laparoscopy presented serve to provide a better understanding of the subject matter and the field of either discipline. Questions that arise in the field of endoscopy from physicians can be addressed at an early stage and, if necessary, worked on with little effort. Ideas for new instruments and even new interventions can arise and be pursued through an intensive exchange between technicians and physicians. Collaborations are established on a local basis which will foster national activities in this field and which allow to develop competencies relevant for the industrial sector of medical engineering.

Optical Coherence Tomography (OCT) is a noninvasive technique for visualizing retinal cross-sections, assisting in diagnosing and monitoring retinal diseases. This study presents an effective OCT image noise reduction and segmentation method. Due to the high resolution of our primary image database, a visual comparison of filter performance was challenging. To address this, we utilized a high-noise image dataset from Duke University, enabling a better evaluation of noise reduction filters. Our method was compared against eight widely-used image filters, including Gaussian, Low pass,Wavelet domain filtering, Lee filter, Anisotropic diffusion, Bilateral filter, Total variational filter, and BM3D filter. Both visual and quantitative analyses were conducted using no-reference performance parameters, namely Gradient Magnitude Similarity Deviation (GMSD), Standard Deviation of Wavelet Coefficients (SDWC), Focus Measure with Tengrade Variance (FMTV), Perception-based Image Quality Evaluator (PIQE), and Visual Information Fidelity (VIF). The results demonstrated the superiority of our proposed filter in terms of noise reduction performance while maintaining the sharpness of retinal layers. Quantitative analysis revealed notable performance gains, including improvements of 63.27% to 83.24% with GMSD, consistent edge strength enhancement of 9.4% to 9.97% using SDWC, gains in image quality between 51.99% and 54.64% with FMTV, performance improvements ranging from 7.25% to 23.97% in terms of PIQE, and a substantial increase in performance varying from 16.31% to 27.69% when assessing the impact on retinal layer quality using VIF. Using the Kruskal-Wallis test, our proposed noise reduction method shows statistical significance for all quantitative parameters. Additionally, our clustering algorithm effectively separated the foreground, including retinal layers and vitreous detachment, from the background and identified an area representing the region between retinal layers where fluid accumulates. We’ve successfully achieved OCT image enhancement, along with distinct foreground and background segmentation.

In current implantology, calcium phosphate cement (CPC) is increasingly used. A special focus is given to CPC scaffolds, as they are suitable for cell settlement and growth due to their positive osteoconductive properties. The design of the scaffolds is of decisive importance for this. The value ranges of the geometric parameters of these scaffolds (e.g. path distance, path diameter), which are positive for both cell settlement and cell growth, are very small. Manufacturing deviations therefore have a significant impact on cell settlement and growth. The pasty manufacturing consistency can cause sagging at the path interstices of a layer immediately below, resulting in significant manufacturing deviations. A larger path distance and thus a larger path interstitial space promotes cell settlement, but at the same time increases the risk of CPC path shape inconsistency. This in turn can have a negative effect on cell settlement. Therefore, the aim of this paper is to investigate the discrepancies between the nominal and actual state at a path distance favourable to cell settlement on the basis of manufactured CPC scaffolds. In this context, geometric and manufacturing parameters of the shape deviation are to be identified and constructive design adaptations are to be derived on the basis of these. In addition, the effects of the shape and position deviations on the flow behaviour will be investigated.

Real time physiological signals like Electrodermal Activity (EDA), measured using wearable devices, could be used for emotion recognition, thus enabling new paradigms of health monitoring, including mental health. This paper analyses EDA signals, a measure of sympathetic nervous system activity, which plays a significant role in emotional regulation. A public database was used to categorize emotions based on valence and arousal values. The Continuous Wavelet Transform (CWT) was used to obtain the scalograms of the signals. The scalograms were then fed into the deep learning models, VGG16 and ResNet50, to recognize emotions. VGG16 gives a Pearson Linear Correlation Coefficient (PLCC) value of 0.7199 for valence and 0.7593 for arousal, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 0.7653 and 1.0284, respectively. ResNet50 performs better, with a PLCC value of 0.7763 for valence and 0.8207 for arousal, and lower MAE and RMSE values of 0.7158 and 0.8712, respectively. This study proposes an adapted ResNet50 model for emotion recognition which could be integrated into wearable devices as a prospective feature.

The overall complication rate during laparoscopic access is estimated to be as high as 14 %. Surgeons have to rely heavily on their experience and haptic perception while inserting the Veress needle or a trocar into the peritoneal cavity. Surgical Audio Guidance (SURAG) is a promising alternative to current techniques. It acquires instrument-born vibroacoustic (VA) waves to track the insertion of the instrument and provide real-time feedback to surgeons. This article presents an initial evaluation of the SURAG technology through two sets of experiments to classify Veress needle events using different AI-models. The results demonstrate the feasibility of using AI for classifying Veress needle events and the potential of the SURAG technology to support surgeons during laparoscopic access and minimally invasive needle interventions in general.

With the recent increase in interest in machine learning and computer vision, camera-based pose estimation has emerged as a promising new technology. One of the most popular libraries for camera-based pose estimation is MediaPipe Pose due to its computational efficiency, ease of use, and the fact that it is open-source. However, little work has been performed to establish how accurate the library is and whether it is suitable for usage in, for example, physical therapy. This paper aims to provide an initial assessment of this. We find that the pose estimation is highly dependent on the camera’s viewing angle as well as the performed exercise. While high accuracy can be achieved under optimal conditions, the accuracy quickly decreases when the conditions are less favourable.

Accurate calibration is key for any reliable sensor system. Magnetoelectric (ME) sensors, in particular, are influenced in their operating point by external parameters such as the Earth’s magnetic field or the ambient temperature. In this paper, we introduce a new planar coil design for the generation of a magnetic test field within the plane of the ME sensor. Furthermore, we implemented a method for measuring the sensor behaviour using a short-term magnetic noise signal. The combination of the printed circuit board (PCB) coil and the accelerated sensor characterization method allows the sensor system to be calibrated at the measurement site (in situ) without the need for laboratory equipment. We can show that the presented method for calibration achieves high-quality results in 10 seconds for a sensor affected by external interference fields.

The modified Allen test (MAT) is a widely used tool to examine the perfusion capacities of the arteries (ulnar or radial) of the hand. In clinical practice, MAT is utilized to ensure that in the event of occlusion, remaining vessels guarantee sufficient hand perfusion. This work shows that perfusion assessment via imaging photoplethysmography (iPPG) can be used to assess MAT. We captured the hands of seven subjects with a standard RGB camera while performing a MAT. Out of these captured video recordings, the iPPG signal was extracted via plane-orthogonal-to-skin transformation. Based on these extracted signals, we derive the heart rate, signal-to-noise ratio, DC parameter, and correlation with a reference iPPG signal to determine if an Allen test was pathological or inconspicuous. For all seven subjects, the results of our MAT assessment were identical to the medical expert’s assessment. This paper presents a procedure and initial thresholds for objectively evaluating and quantifying MAT.We show how globally and locally calculated parameters can be used to assess the perfusion of the investigated hand. This analysis makes it possible to quantify the exact point in time when the perfusion is again sufficient. If a reperfusion problem occurs, we are able to localize the spatial regions of occurrence. The presented hardware setup is affordable and can be easily installed and used in every doctor’s office.

Introduction: Neural organoids promise to help understand the human brain and develop treatments for neurological diseases. Electrophysiological recordings are essential in neural models to evaluate the activity of neural circuits. Mesh microelectrode arrays (MEAs) have been demonstrated to be suitable for organoids and spheroids, and there is demand for easy-to-use devices that can be manufactured at scale. Methods: We present a new mesh MEA device with an easyto- use design. We produce mesh MEA chips on 100 mm carrier wafers and connect individual chips to PCBs by wirebonding. The devices are completed by assembly of a twopiece well and a glass cover slip. Results: Each device contains a suspended hammock-like mesh with 64 microelectrodes. The square grid’s pitch of 200 μm makes the mesh suitable for typical organoid sizes while spreading the electrodes across a 1.4 mm region. The well is designed for fluid handling by pipetting or pump systems. Impedance measurements indicate a high yield of functional microelectrodes, although further effort is needed to produce consistent low impedances. The devices are compatible with commercial amplifiers, while adaptation of the PCB to other formats will be straightforward. Conclusions: Using scalable production methods, we have developed a mesh MEA device design that offers improved ease-of-use. Next steps will include biological validation in collaboration with partners.

Optical Coherence Tomography (OCT) is a standard imaging procedure in ophthalmology. OCT Angiography is a promising extension, allowing for fast and non-invasive imaging of the retinal vasculature analyzing multiple OCT scans at the same place. Local variance is examined and highlighted. Despite its introduction in the clinic, unanswered questions remain when it comes to signal generation. Multiphase fluids like intralipid, milk-water solutions and human blood cells were applied in phantom studies shedding light on some of the mechanisms. The use of hydrogel beads allows for the generation of alternative blood models for OCT and OCT Angiography. Beads were produced in Hannover, their size was measured and their long term stability was assessed. Then, beads were shipped to Karlsruhe, where OCT imaging resulted in first insights. The hydrogel acts as a diffusion barrier, which enables a clear distinction of bead and fluid when scattering particles were added. Further on, the scattering medium below the bead showed increased signal intensity. We conclude that the inside of the bead structure shows enhanced transmission compared to the plasma substitute with dissolved TiO2 surrounding it. Beads were found clumped and deformed after shipping, an issue to be addressed in further investigations. Nevertheless, hydrogel beads are promising as a blood model for OCT Angiography investigations, offering tunable optical parameters within the blood substitute solution.

Patient’s vital data will have an important role in future medicine. Best way to store this data is the patient’s electronic health record (EHR) in a structured form. In Germany, several laws, regulations and standards must be considered to store vital data in the health record, like SGB V, DIGAV and Medical Information Objects. In the near future, it will be possible to upload self-recorded vital data to the German EHR special smartphone apps. It will also be possible to automatically evaluate this data using machine learning.

Fallopian tube occlusions represent one of the most common causes of female sterility. As an innovative treatment approach for affected persons, we previously presented the concept of a novel polymeric, self-expanding, and bioresorbable microstent. As a basis for microstent development, knowledge of the mechanical properties of the anatomical target structure represents a crucial requirement. The current work describes a methodological approach for the experimental determination of radial Fallopian tube compliance using optical coherence tomography. It could be shown that a quantitative assessment of the mechanical properties of porcine Fallopian tube samples - as a whole anatomical structure including the Tunica mucosa, the Tunica muscularis, and the Tunica serosa - is possible, using the described test setup. Future investigations on human samples will allow for valuable information regarding the structural-mechanical properties of the Fallopian tube. Therefore, the current work offers perspectives for the development of a novel gynecological microstent for the treatment of Fallopian tube occlusions.

Respiration is a vital process for normal function at every level of organization of living beings. A simple and noncontact measurement of the respiratory rate certainly offers advantages in many situations compared to classical measurements, e.g., with a respiratory measurement belt. Thus, we started a study for thermographic detection of breathing rate. For this purpose, several subjects were filmed with an infrared (IR) camera and the recordings were subjected to pixel value analysis using MATLAB. By using different regions of interest (ROIs), the pixel values were averaged and mapped graphically. The resulting graphs generally show a periodic breathing process based on the temperature changes within the selected ROIs. To put this pixel value analysis into perspective, control measurements were taken with a BIOPAC-system using a respiratory measurement belt to measure abdominal/thoracic respiration. The two sets of measurements were compared by cross correlation and a moderately strong anti-correlation was demonstrated.

Fluorescence microscopy based cell painting technique profiles the morphological characteristics of specific cell organelles with high resolution. However, photo toxicity, photo bleaching and advanced instrumentation limits its utility for comprehensively annotating the cell structure. Generating cell painted organelles from simple and least invasive transmitted light microscopy provides a surrogate for clinical applications. In this study, the employability of semantic segmentation model for delineating the nuclei from composite image using UNet++ is investigated. For this, a public dataset consisting of 3456 composite images are considered from Broad Bioimage Benchmark collection. The binary masks of endoplasmic reticulum (ER), nuclei and cytoplasm are generated for pixel wise labelling. The composite images and their labelled dataset are fed to the UNet++ model for segmenting the cell painted nuclei. The performance of the deep semantic network is analysed for 50 epochs and the segmentation results are validated with mean intersection-over-union (IoU). The UNet++model provides an accuracy of 95.8% with a minimum loss of 0.1. Mean IoU of 0.91 is achieved for the prediction of nuclei from the composite images. The obtained results suggest that this study could be employed for predicting the subcellular components from transmitted light microscopy without the use of fluorescent labelling.

The shortage of operating room technicians has led to a growing demand for automated systems in the OR to maintain the quality of care. Robotic scrub nurse (RSN) systems are increasingly being developed, which perform tasks such as handling instruments and documenting the surgery. While research has focused on detecting instruments in the hands of surgical staff or recognizing surgical phases, there is a lack of research on detecting instruments on the instrument tray. Therefore, this study proposes and evaluates two distinct methodologies for instrument detection on the OR table using the deep learning approaches YOLOv5 and Mask R-CNN. The performance of the two approaches has been evaluated on 18 YOLOv5 models and twelve Mask R-CNN models, mainly differing in model size. Two sets of instruments were used to assess generalizability of the models. The results show a mean average precision (mAP) score of 0.978 for YOLOv5 and 0.846 for Mask R-CNN on the test dataset comprising three classes. An mAP of 0.874 and 0.707 have been computed respectively for the test dataset including six classes. The study provides a comparison of the performance of two suitable approaches for instrument detection on the instrument tray in the OR to enhance the development of RSN systems.

The electrocardiogram is a very valuable clinical tool which allows to retrieve information about the presence and location of arrhythmic foci as well as ischemic and scar tissue and disorder’s of the dedicated cardiac conduction system. In the presented study timing parameters computed by a delineating beat detector for identifying the P-Wave, QRS - complex and T-Wave are used to classify the individual beats. From a set of total 419 feature generated from these parameters 64 are used to train LDA classifier for discriminating 3 classes (Normal, Artifact, Arrhythmic) and 5 Classes (Normal, Artifact, Atrial and ventricular premature contractions and bundle branch blocks). Further it is investigated how the imbalance between normal beats and arrhythmic beats as well as the beats missed by the beat detector affect the classification results. In the case of 5 classes accuracies of 97.52 % in the imbalanced case and 96.38 r for the balanced data were obtained. For 3 classes accuracies of 97.76 % and 95.18 % were achieved. Considering in addition the beats missed by the detector the accuracies dropped to 96.68 %, and 95.54 % for 5 classes and 95.54 % and 96.92 % for 3 classes. These values are within the ranges for linear classifier reported in literature. This is quite promising for implementing a real-time classifier which exploits the parameters and values computed by the beat detector.

Female sterility is caused by Fallopian tube occlusions in one of three cases. Current treatment methods achieve unsatisfying pregnancy rates and are associated with high costs or significant psychological and physiological stress. Our previously described microstent technology opens up new therapeutic possibilities to restore the lumen of the Fallopian tube without surgery. In this work, a Finite Element Analysis model of a physiologically relevant loading of the device is presented. Therefore, microstent radial force was analysed as a function of the device diameter up to a minimum diameter of 1.4 mm, which was found after microstent implantation ex vivo . A bilinear constitutive material model considering isotropic hardening was used for modeling of the microstent. The Finite Element Analysis results were validated using corresponding results of experimental investigations. In this context, a maximum deviation of 15% between experimental radial force and the corresopnding simulation results (1.33 N vs. 1.13 N) was found. Maximum von Mises stresses of approximately 88 MPa were determined. A good agreement between simulation and experiments was found. Therefore, future simulations will be carried out as a basis for microstent design optimisation.

Hyperspectral imaging is an emerging technology that has gained significant attention in the medical field due to its ability to provide precise and accurate imaging of biological tissues. The current methods of hyperspectral imaging, such as filter-wheel, snapshot, line-scanning, and push-broom cameras have limitations such as low spatial and spectral resolution, slow acquisition time. New developments on the field of light field cameras show the potential to overcome these limitations. In this paper, we use a novel hyperspectral lightfield camera and try to combine the capability of hyperspectral and 3D analysis. For this purpose we calibrate our system and test it during two ENT-surgeries to show its potential for improving surgical outcomes. The micro-lenses of the camera map 66 spectral sub-images onto the sensor allowing to reconstruct the spectral behavior of the captured scene in the spectral range of 350-1000nm. In addition, we use the sensor data to apply a 3D camera calibration pipeline to allow 3D surface reconstruction. We captured 26 calibration images and achieved calibration results in accordance to stated company data. The best calibration showed a re-projection error of 0.55 px. Further, we tested the camera during a parotidectomy and a neck-dissection. The extracted reflectance spectra of the selected venal and arterial regions correspond perfectly to the spectrum of oxygenated and deoxygenated hemoglobin. For the first time, up to our knowledge, a hyperspectral lightfield camera has been used during a surgery. We were able to continuously capture images and analyze the reconstructed spectra of specific tissue types. Further, we are able to use the sensor data of the micro-lens projections to calibrate the multilens camera system for later intraoperative measurement tasks.

In image-guided surgery, imaging systems such as (stereo-)endoscopes allow intra-operative 3D reconstructions in form of point clouds. However, endoscopes provide only a narrow field of view, resulting in a confined point cloud. In this paper, we present an analysis and registration pipeline for confined point clouds acquired by stereo endoscopes into a fused representation. For a coarse registration, TEASER is applied, while a refinement is conducted utilizing point-to-plane ICP. The pipeline is tested on two datasets: acquired point clouds of a head phantom using a EinsteinVision ® 3.0 endoscope and point clouds from the Stereo Correspondence And Reconstruction of Endoscopic Data challenge. The results for both datasets show that 3D reconstructions of anatomical structures by utilizing stereo-endoscopes and point cloud registration is a promising contactless and radiation-free method. However, non-rigid deformations are not yet incorporated and evaluation of the method compared to reference data is challenging.

During cerebral revascularization surgeries, blood flow values help surgeons to monitor the quality of the procedure, e.g., to avoid cerebral hyperperfusion syndrome due to excessively enhanced perfusion. The state-of-the-art technique is the ultrasonic flow probe that has to be placed around the blood vessel. This causes contact between probe and vessel, which, in the worst case, leads to rupture. The recently developed intraoperative indocyanine green (ICG) Quantitative Fluorescence Angiography (QFA) is an alternative technique that overcomes this risk. However, it has been shown by the developer that the calculated flow has deviations. After determining the bolus transit time as the most sensitive parameter in flow calculation, we propose a new two-step uncertainty reduction method for flow calculation. The first step is to generate more data in each measurement that results in functions of the parameters. Noise can then be reduced in a second step. Two methods for this step are compared. The first method fits the model for each parameter function separately and calculates flow from models, while the second one fits multiple parameter functions together. The latter method is proven to perform best by in silico tests. Besides, this method reduces the deviation of flow comparing to original QFA as expected. Our approach can be generally used in all QFA applications using two-point theory. Further development is possible if number of dimensions of the achieved parameter data are broadened that results in even more data for processing in the second step.

Introduction: Falls and gait disorders often result in hospitalization and immobilization. Near-falls may be one of the earliest signs of increased fall risk. In the literature, several sensor positions are used for fall detection, but few studies include the head as a sensor position. Hearables and hearing aids are increasingly equipped with inertial measurement units (IMUs) and are therefore of particular interest for measuring the risk of falling in everyday life. Methods: Therefore, we investigate the suitability of the ear as a sensor position for near-fall detection in comparison to the standard sensor positions. The motion data of one study participant (female, 63 years) was exemplary analyzed. The participant walked at her individually preferred gait speed on a perturbation treadmill while nine different perturbations (anterior-posterior, medio-lateral and pitch) were applied with a time interval of 20-30 seconds. We used seven IMUs during the measurement at the positions ear, sternum, lumbar, wrist (left/right), foot (left/right). Results: The absolute acceleration signals at the seven different positions show the periodicity of the normal gait before the perturbation. During and after the perturbation changes in the motion pattern can be seen, whereby the response to the perturbation occurs with a slight time lag. The Pearson correlations show that the sensor positions sternum, lumbar and ear correlate well with each other and thus show similar signal characteristics in the reaction to this perturbation. Conclusion: This provides evidence that the ear sensor position is at least comparable to the preferred sensor positions in the literature on the torso. However, these results were obtained under laboratory conditions. Further research is needed to investigate the sensor position at the ear in everyday life.

Laser-induced periodic surface structures (LIPSS) provide the possibility to functionalize the surface of various materials, which can be used to control the wettability of metal surfaces or influence cell adhesion. LIPSS thus offer great potential for application on implant surfaces if their fabrication process is compatible with standard implant fabrication techniques. Here we demonstrate an easy-to-use two-step fabrication method for stents with different types of LIPSS. The separation between the fabrication of the stent structure and the surface structuring process allows the implementation of any stent structure finishing technique without affecting the LIPSS. The suggested fabrication method may be a step further in implementing laser machining-based surface functionalization in commercial implants.

This paper presents the use of vibro-acoustic sensing to augment laparoscopic surgery procedures by analyzing the signals produced during cutting and palpation tasks on various tissue samples. Vibro-acoustic signals were acquired during an experiment on a dedicated phantom covered in dense foam, where three trocars were inserted into the phantom to place the endoscopic camera and two laparoscopic instruments. The results of the signals analysis demonstrate the potential of this approach for making laparoscopic interactions audible, differentiating between tissue types, and detecting variations in tissue properties. Vibro-acoustic sensing could be a valuable tool for integrating sound into the current clinical workflow for enhancing endoscope video images.

The use of state-of-the-art technology in emergencies enables fast assessment of the situation. Modern rescue robots, but also rescuers with camera systems, record vast amounts of image data. In addition to the benefits for rescue operations, a quick overview based on this data can help medical staff in the search for suitable treatment strategies. However, such data must be examined and fused into an intuitive display. In this work, exemplary image streams of RGB-D cameras are searched for victims and transferred into separate 3D models per detected person for subsequent intuitive viewing, rotating, and zooming. The method was tested and preliminary evaluated for its functionality, detail accuracy and readability of textures at the German Rescue Robotics Center.

The separation of bio-suspensions, such as blood, is of great interest for many biomedical applications, such as the extraction of leucocytes from whole blood, the laboratory analysis of specific cells or a mixed cell suspensions from bioreactors or the preparation of cellular products for cellbased therapy. In particular, mechanical separation processes have been established for the rapid separation of biosuspensions. The principle of mechanical separation is based on the specific density of the particles or the particle size. Thus, the corresponding processes are based on centrifugation and/or filtration. The standard process to prepare blood products from whole blood donations contains several steps. First the blood is separated into different phases by centrifugation and then further separated by filters containing specific filtration layers. The shortcomings of this process is the low yield due to bio-particles remaining in the filter and the high costs for equipment due to the use of multiple devices for centrifugation and filtration. Here we present the proof of principle of an adaptive, size-selective filter that addresses the above mentioned shortcomings. The filter is designed to combine the filtration and centrifugation process within one step. Therefore a novel filtration device was designed that can be used within a standard centrifugation tube. During centrifugation, the centrifugal force changes the filtration coefficient of the filter. This can be used to separate different sized of bio-particles at different centrifugation speeds. Moreover, the cells that were hold back in the filter can be removed afterwards by increasing the centrifugation speed. In this way, the filter is created to gradually release cells of a specific size yielding in a high purity of different cell fractions.

Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set up and sufficiently train a deep CNN from scratch. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. A convolutional neural network (CNN) and a Bidrectional long short term memory network (BiLSTM) are integrated for automatic feature extraction and sleep stage scoring using only a singlechannel EEG signal. To demonstrate the generalizability of our results, the proposed model was evaluated using PSG records of 81 patients that were collected in different environments, through different recording hardware, and annotated with different sleep experts. The use of a single EEG source and a one-to-one classification scheme in the proposed model can allow further development towards wearable systems and online in home monitoring applications.

Conventional mechanical drilling for implant placement is limited to specific geometry and orientation, resulting in incorrect positioning and inadequate anchoring in thin or porous bone. This study presents the development of a laser-based ablation process and an applicator technology for precise and fast laser drilling in the oral cavity. For the process development, a CO2 laser with a wavelength of 10.6 μm was used. Pulse durations between 10 μs and 400 μs were investigated for fast laser drilling with low thermal impact. For efficient ablation and cooling of the bone tissue, we applied a fine water spray. The laser applicator is designed with an integrated scanning module, focusing optics and a compact water spray system with three spray nozzles in the applicator tip. The geometry of the cavities was analyzed using digital microscopy and scanning electron microscopy, allowing to measure the ablated volume and depth as well as investigating the bone microstructure. This study demonstrates a laser ablation process capable to generate cavities with an ablation rate of 1.75mm3/s which is about 80% higher than previously reported. At this ablation rate the melted zones were smaller than 30 μm. This paper demonstrates a concept for a dental laser drilling system with a fast ablation process and a highlyintegrated applicator for treatment in the oral cavity.

Introduction: In this study, phase-contrast magnetic resonance imaging (PC-MRI) 4D flow data for patients with bicuspid aortic valve (BAV) were analyzed based on the spatial resolution of the images. BAV is a congenital heart defect characterized by the presence of two cusps in the aortic valve, leading to various complications. PC-MRI is a noninvasive imaging technique used to assess the hemodynamics in patients. However, interpretation of PC-MRI data can be challenging due to complex hemodynamics, which makes numerical simulations necessary to complement the results. Methods:Within this study PC-MRI 4D flow data in the aortic arch (AA) were compared with hemodynamic numerical simulations based on the MRI resolution for six patient-specific AAs with BAV. First, the segmentations were reconstructed and divided into three groups based on the resolution. Second, the numerical simulations were performed and resulting hemodynamics compared to PC-MRI results at three time points in the cardiac cycle using velocity and wall shear stress. Results: Results from group 1 (highest resolution) show an overestimation of wall shear stress (WSS) values during peaksystole in the three arteries and along the AA and descending aorta (DAo). Nevertheless, velocity mean values show an overall good agreement. Group 2 shows higher velocity magnitudes in the simulation than in PC-MRI. Within group 3, velocity values differ from the measurements. Conclusion: The study shows that with a low resolution, flow patterns can already be evaluated based on PC-MRI, but to get an insight into the flow quantitatively a higher resolution is necessary.

Home rehabilitation is a great complement to inpatient rehabilitation, because it allows for more regular training, especially in rural areas, where the access to physiotherapists is limited. However, unsupervised training carries the risk of deviation from the prescribed protocol. An obvious solution is to introduce a technical means for rehabilitation and continuous monitoring of performance. This paper presents a robotic system for rehabilitation of three degrees of freedom of the wrist independently, implemented with only one actuator, optimizing the design and size of the system. The device provides measurements of joint torque-position dependence, allowing objective assessment of mechanical impedance and range of motion to evaluate the progress of rehabilitation. Derived from research on haptic devices, a measurement method was implemented to measure hand impedance in the high frequency range between 100 and 200 Hz using a combination of exciters and accelerometers in the handles. This allows continuous monitoring of gripping forces without the need for complex contact-based force sensors. In collaboration between the authors’ technical and medical universities, a set of two-level passive and active safety measures was implemented to ensure the safety of the system’s users. Therefore, the presented system shows the potential to evaluate the mechanical impedance of the wrist at both low and high frequencies and to monitor the progress during rehabilitation. In the next step, a baseline of wrist impedance will be measured in a target group of healthy volunteers in order to prepare for studies in motion-restricted subjects.

The AUC margin loss is a valuable loss function for medical image classification as it addresses the problems of imbalanced and noisy labels. It is used by the current winner of the CheXpert competition. The CheXpert dataset is a large dataset (200k+ images), however datasets in the range of 1k-10k medical datasets are much more common. This raises the question if optimizing AUC margin loss also is effective in scenarios with limited data.We compare AUC margin loss optimization to binary cross-entropy on limited, imbalanced and noisy CheXpert5000, a subset of CheXpert dataset. We show that AUC margin loss is beneficial for limited data and considerably improves accuracy in the presence of label noise. It also improves out-of-box calibration.

This study presents a novel approach for threedimensional (3D) cell culture using a two-component system consisting of a 3D-printed scaffold structure combined with a perfusion channel. A polymeric scaffold structure with an overall size of 9 mm x 9 mm x 1 mm composed of a cubic lattice with a web thickness of 200 μm and pore size of 600 μm was 3D-printed using a hot UV-stereolithography (SLA) system. The perfusion channel with an inner diameter of 800 μm and channel wall pores of 300 μm for cell culture medium supply was 3D-printed with the same system and material. Scaffolds were investigated with respect to the printing accuracy by digital microscopy. Cytotoxicity of the materials was assessed using MTT-assay and Live/Dead staining. Scaffold were subsequently seeded with 3T3- fibroblasts within a fibrin-based hydrogel and then conditioned either statically or under passive perfusion using a hydrostatic pressure driven flow system. The results show that the scaffold structure and perfusion channel can be produced with high accuracy and stability allowing a supply of nutrient and oxygen via perfusion channel to the cells within the scaffold. This approach has potential for nutrient supply within larger constructs for tissue engineering and regenerative medicine applications.

Due to the increasing average age of the population, the number of implants is also increasing and with it the number of explantations. Therefore, facilitated implant removal is of great interest. A nanocomposite consisting of superparamagnetic core-shell nanoparticles (CSNPs) and a synthetic polymer is supposed to be used as implant coating, aiming for a stimulus-inducible modification of the composite’s rheological properties by hyperthermia. Here, the first steps following this concept, the synthesis and modification of the CSNP are reported. In this work magnetite nanoparticles build the core and are surrounded by a periodic mesoporous organisilica (PMO) shell. For this reason, the CSNP are referred to as magnetic PMO (mPMO) particles in the following.

The removal of the cochlear implant (CI), which in some cases is without alternative, is still an act of simple pulling, not only causing harm for the patient by damaging tissue but also making reimplantation more difficult. For that reason, it is necessary to develop mechanisms to make an explantation easier. To overcome this problem adaption of the mechanical properties by light-degradable periodic mesoporous organosilica (PMO) can be one solution. By introducing PMO nanoparticles into the CI’s silicone matrix, the particles act as a stiffening agent, which can be degraded by irradiation with UV light. Using this mechanism, the silicone becomes softer, thus making explantation easier and safer for patients. Here first results, concerning the creation of a silicone composite material with light-sensitive adaptive mechanical properties are reported.

This study investigates the application of joint bilateral filtering for noise reduction in fast kVp switching dualenergy computed tomography (DECT). A high resolution image is calculated from non-congruent data of two scans with different energy spectra. This image guides the denoising process of the material decomposition that are used to obtain virtual monoenergetic images (VMIs). The performance of the filter was evaluated in terms of standard deviations on measured data. The proposed filter significantly reduces noise in the VMIs and preserves edges. Furthermore, the simple implementation of the filter can be used for various scanning protocols.

In recent decades ballistocardiography (BCG) has regained popularity as a way to measure the mechanical activ- ity of the heart. In this paper we present and evaluate a new iteration of our mobile BCG measurement system. The pro- posed system uses an inertial measurement unit (IMU) placed on the carotid artery to derive the BCG. We conducted a mea- surement series to evaluate the system and to do an initial in- vestigation into whether more complex heart parameters can be derived from BCG data. The evaluation shows that heart rate (HR) and heart rate variability (HRV) calculated using BCG data, agrees well with the reference measurement and amplitudes calculated are mostly comparable in range to other papers. In conclusion, the system can reliably derive features from BCG data that can be used in further research.

Differentially Expressed Genes (DEGs) are treated as candidate biomarkers, and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we used a combination of the machine learning algorithm and statistical tests to identify the top 300 genes that are differentially expressed in ASD compared to Typically Developed (TD). Initially, we extracted microarray gene expression data of 15 ASD and 15 TD from NCBI GEO database and used a standard pipeline to preprocess the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We, then analyzed the upregulated and downregulated genes using the logFC value to gain insights into their potential roles in the development of ASD. We further used drug-gene interaction analysis from ConnectivityMap to identify drugs that can inhibit the expression of these genes. Our results show that the proposed RF model yields average 5-fold cross-validation accuracy, sensitivity, and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Our analysis identified several novel genes that are dysregulated in ASD, including genes (such as proliferation-inducing protein 38 and germinal centre expressed transcript 32) involved in synaptic transmission, neural development, and immune function. We also identified several drugs (such as ATPase_Inhibitor, kinase inhibitors, and histone deacetylase inhibitors) that can potentially be used to treat ASD. Our findings provide new insights into the molecular mechanisms of ASD and suggest potential targets for drug development. These findings may lead to new therapeutic approaches for the treatment of ASD.

Elastomer based high compliant balloons containing strain sensing element(s) (SEs) is currently under development intended for in-vivo biomechanical diagnostics of vessels. It could potentially reveal local lumen features based on patterns derived from the sensing elements. A Finite Element based, simulation study in COMSOL® (v5.6) focusses on in-vivo inflation behavior of an elastomeric balloon being equipped with SE, whose compliance is ideally magnitudes higher than the surrounding tissue in an idealized 2D setup. We hypothesized the vessel’s inner wall as a closed convexconcave 4-fold structure correlated to surface structures found in urethrae and parameterized the fold depth. A set of SEs consisted of one SE over the inner surface of balloon while the other over the outer wall. Out of the three adjacent placed sets, The first set was closer to the tissue lumen while the third set the farthest. We assessed the stretch of balloon over its inner circumference through SEs. At conformal contact with the tissue wall, The first SE shows a higher value, while the third element undergoes the least stretch within the three sensing elements. The SE’s over the outer circumference show the exact opposite relation. The differences between the sensing elements over the inner and outer circumferences show a correlation with the curvature of the tissue it conforms to. With use of balloon that was 10x thicker (10μm vs 1μm), around 10x larger stretch differences were captured suggesting possibility to use less sensitive measuring system. For the ideal situations performed, the curvature and depth information may be comprehended by observing differences between inner and outer SEs while thicker balloons prove to be more useful to generate differences that are easier to capture in practical situations. This semiquantitative study suggests that simulation studies are powerful tools to obtain new analytical techniques for shape characterization.

The neuronal activity has a unique genetic signature that can be used for personal identification and authentication. Continuous authentication of individuals is required in the field involving high security, such as military services, intelligence organizations and secret agencies. Electroencephalogram (EEG) based authentication method is favorable due to its uniqueness and the fact that it can be used even when the person is unconscious. In this study, we investigated the number of samples per subject required to reliably develop a biometric trait authentication system. Initially, we extracted the background EEG signals from the publicly available Temple University Hospital (TUH) database. A total of 46 statistical and frequency domain features were extracted from each EEG signal per subject. The classification was performed using the extreme Gradient Boosting (XGBoost) classifier. We varied the number of EEG signal segments per subject from 5 to 127 with an increment of 5 segments per trial. Finally, the performance parameters such as accuracy, sensitivity, specificity, precision and F-measure were obtained in each case for the biometric authentication system using the test data. Our model achieved the highest accuracy of 100% when more than 75 EEG signals per subject were considered for the analysis. We also identified alpha band power as the most efficient feature for authentication. Our results show that EEG signals can be effectively used for personal identification and authentication.

Emotions are mental states that result from neurophysiological changes associated with thoughts, feelings, and behavioral responses. Emotions lead to modifications in heart rate variability, which can be identified through electrocardiogram (ECG) signals. In this study, we attempted to analyze the ECG signals to detect categorical emotions using time-domain features and machine-learning algorithms. Initially, the ECG signals of 30 subjects were obtained from the publicly available continuously annotated signals of emotion dataset. Further, the signals were preprocessed and extracted 32-time domain features from ECG signals which were recorded during different emotional states such as amusing, boring, relaxing, and scary. The extracted features were fed to a random forest (RF) classifier to rank the features and to build the three machine learning models such as logistic regression (LR), support vector machine, and RF. We achieved the highest average classification accuracy, sensitivity, specificity, precision, and f1-score of 71.04%, 42.08%, 80.69%, 43.03%, and 42.32%, respectively, with the top 4 features using the LR classifier. We found that the mean of peaks, slope sign change, dynamic range, and mean of first derivative were ranked top and played a significant role in the classification model. Our study shows the effectiveness of utilizing ECG signals for emotion detection in wearable devices.

The augmentation of smart insoles gained increased interest due to the possibilities in foot-related rehabilitation, medical treatment, and postural risk prevention. As shown by previous work, the technology offers a personalized solution for individual foot morphology and biomechanics, which can monitor the health status and healing process. However, the success of this mobile technology depends on its usability and acceptance by healthcare professionals and patients. Therefore, an exploratory qualitative user study was conducted to investigate the preferences of healthcare professionals and patients regarding augmented smart insoles (ASI) usage. In focus groups, experts and patients explored their needs and suggested potential design solutions. The thematic analysis of the participants’ feedback revealed useful information regarding user interface (UI) designs for the development of a mobile application supporting smart insoles. We found that patients and experts share similar data preferences for a mobile application supporting ASI and that both user groups desired the presence of complete pressure point monitoring, active feedback for posture correction, and personalized training videos for postural risk prevention. However, healthcare professionals expressed concerns regarding the lack of scientific evidence validating the effectiveness of this technology and suggested further research.

Wearable emotion recogniton system is essential in identifying mental health disorders by early detection and continuous monitoring of human emotions to provide proper treatment care. Electrocardiogram (ECG) signals can be used for emotion recognition for its noninvasiveness and easy usability. In this study, Detrended Fluctuation Analysis (DFA) and Extreme Gradient Boost (XG Boost) classifier is used to classify the scary and boring emotion from the ECG signals. For this, ECG signal corresponding to these emotions are obtained from public database. The preprocessing is performed by adding the video IDs to the signal and annotating it. This preprocessed signal is subjected to DFA to understand the power-law correlations and similarity property. Further, from the power law correlations, features namely Hurst exponent and DFA intercept are extracted. These features are subjected to XG Boost classifier to differentiate the two emotions. Results shows that the log-log plot of power law correlation is linear in nature which indicates that ECG signals of both the emotions have long range correlations and self-similarity property. The extracted scaling exponent indicates variations between scary and boring with a mean and standard deviation of 0.81±0.13 and 0.68±0.07 respectively. Similarly, DFA intercept provides mean and standard deviation for scary and boring 0.15±0.06 and 0.17±0.07 respectively, showing less variability in the ECG signal. XG Boost classifier gives accuracy of 80.5% for classifying scary and boring emotion. Thus, the proposed approach can be used for wearable emotion recognition system to differentiate scary and boring emotion.

The accuracy of diagnosing and monitoring eye diseases using fundus imaging is strongly dependent on the quality of the images. Poor image quality can result in delays or inaccuracies in diagnosis, thus risking patient health. Image quality assessment (IQA) is crucial for regulating retinal imaging quality and assisting in successful diagnoses. In this paper, a learning-based model for evaluating the quality of fundus images is proposed. The EyeQ dataset is selected due to its ease of availability and large collection of images. The images in the dataset are classified into three labels based on their perceptual quality. Label 0 denotes good, 1 denotes fair and 2 denotes poor quality. The ResNet50 deep learning model is fine-tuned with different hyperparameters to develop two models, which are labelled as network-1 and network-2. Network-1 uses the Stochastic Gradient Descent (SGD) optimizer and network-2 utilizes the Adam optimizer. Both networks utilise the Categorical Cross Entropy (CCE) loss function, are trained for 40 epochs and has a learning rate of 1e-3. The performance of the trained models is compared using the accuracy validation curve and the loss validation curve. Network-1 demonstrates an accuracy value of 85.8% with a loss of 0.37 and network-2 achieves an accuracy of 86.2% with a loss of 0.34. The results indicate that network-2 slightly outperforms the performance of the other in terms of accuracy, and should be preferred. Moreover, it is evident that the ResNet50 model is highly adept in appropriate feature extraction and quality evaluation of retinal fundus imaging.

Within this paper we demonstrate a novel approach to create antibacterial nanostructures on the implant material titanium grade 23 by a high-pressure oxidation method. Titanium samples were oxidized and resulting nanostructures were characterized using scanning electron microscopy and contact angle measurements. Antibacterial properties were tested using Escherichia coli and Bacillus subtilis as common lab strains. In addition, cytotoxicity was determined according to ISO 10993-5 standards. Results reveal that the fabricated nanostructures have similar antibacterial properties known from different insect wings like dragonflies or cicadas and have no cytotoxic effect.

In orthopaedic technology unilateral leg amputees are provided with prosthesis which are attached through a shaft to the remaining part of the leg. The prosthesis legs are built according to manufacturer’s specifications and get adjusted to the individual needs through motion analysis. If the prosthesis is not adjusted correctly to the individual needs of the user, the chance for development of arthritis in the remaining knee joint is greatly increased. The goal of this paper is to develop a procedure based on AEA to detect signs of arthritis in the remaining knee joint early and the base of a procedure for a future assessment of the quality of the care. The measurement system consists of the AEA - measurement device, a force plate to measure ground reaction forces, two strain gauges which are attached to handles on a test rig and two video cameras which record the movement from frontal and sagittal view. The amputee performs a standard movement in the test rig which consists of three knee bends in ten seconds while wearing the prosthesis legs. The test rig is provided so the amputee can support themselves using arms and hands as prosthesis’ support during the extension phase of the knee bend. The posture and load of the knee joint during the movement is analyzed with the video and force data. The ground reaction forces show the load distribution between the remaining leg and the prosthesis and its support during the movement. The hand forces give clues how well the amputee can balance and which arm is favored under load. By combining kinematic data and AEA the state of the load transfer zones can be evaluated. Thus, it’s possible to examine the remaining knee joint for defects and evaluate the quality of the prosthesis adjustment.

Within this work, the bacterial adhesion on biomimetic titanium nanostructures is investigated in fluidic flow. The motile, gram-negative bacteria strain Escherichia coli was used for the experiments and was incubated on nanostructured titanium, polished titanium and on the wings of the dragonfly Neuorbasis kaupi. After incubation period different flow velocities have been applied and the reduction of adhered bacteria was determined via fluorescence microscopy. Results show that nanostructured titanium surfaces have the ability to reduce the bacterial contamination as it is for the dragon fly wings used in this study. This behavior can also be predicted by analyzing the bearing area of the nanotopography which describes the actual part of the topography which is in contact to the bacteria.

This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain likelihood information of the drug resistance which can be utilized computationally. Initially, CXR images are subjected to lung fields segmentation using Reaction Diffusion Level Set method. Further, Local Directional Texture Pattern (LDTP) features are extracted from the segmented lungs to characterize the localized textural variations. Extreme Gradient Boosting (XGBoost) classifier is employed to differentiate DS-TB and MDR-TB images. The obtained results demonstrate the ability of extracted LDTP features to characterize nonspecific textural inhomogeneities in images by operating on its principal directions. XGBoost algorithm provides maximum accuracy of 93% and true positive rate of 94.6% in detecting MDR-TB. As the proposed study differentiates the MDR-TB condition using CXR images, its computerized diagnostics could be used in the early screening and followup of TB ridden patients for public health infection control in any setting.

Introduction: Over the last years, in doubt of cochlear implant (CI) candidacy, assessment of excitability of the auditory nerve is performed with pre-operative tests. These tests are the pre-operative (pre-op.) performed electrical auditory brainstem (EABR) and the late response (EALR) recorded with trans-tympanic promontory stimulation in local anesthesia. But in some cases after such preoperative tests there is still doubt about the excitability of the auditory pathway. The most reliable EABR is recorded with using stimulation via the CI. However, before CI implantation,, an intra-cochlear test electrode could be used in order to achieve similar EABR results. Methods: In doubtful cases of patients who underwent pre-op. EABR, we performed EABR by a test electrode, part of the auditory nerve test system. This measurement was used to confirm the pre-operative results just before CI implantation. Additionally, EABR using CI stimulation was performed intra-operatively right after the CI implantation to confirm EABR using the test stimulation electrode results. Results: Six subjects were included in this study. They were tested by pre-op. EABR (n=6), pre-op. EALR (n=5), intra-op. EABR using test electrode (n=6), intraop. EABR via CI (n=6), and post-op. EABR (n=2), and postop. EALR (n=2). In two cases, intra-operative EABR showed a positive, i.e. clearly measureable, response whereas there was a doubtful response in pre-op. EABR. Intra-op. EABRs (test electrode and CI stimulation) showed the same results in all cases. Waveforms of EABR using test electrode look similar to EABR using CI stimulation but with more defined peaks compare to the pre-op. EABR. In one case with all-over positive EABR, pre-op. EALR, and post-operative EALR were matching. Conclusion: Intra-op. EABR using test electrode was easy to record and it shows similar results to EABR using CI stimulation. Intra-op. EABR using test electrode may help in confirming the results of not clear preop. EABR with a minimal increase in recording time.

Introduction: The trans-tympanic electrically evoked auditory brainstem response measurement in local anesthesia (= LA-TT-EABR) has been shown as a useful tool in doubtful CI candidacy to objectively evaluate the excitability of the auditory pathway up to the brainstem. Previous studies in this matter were of relatively low subjects number. To update the knowledge of the reliability of LA-TTEABR, we re-evaluated the latest results from a bigger subjects dataset from our clinic and follow up regarding hearing sensation post-operatively. Methods: LA-TT-EABR was performed, as described in previous publications, with a trans-tympanic golf-club electrode in the round window niche for pre-operative stimulation in local anesthesia and with an evoked potential device for EABR recording. Hearing sensations were monitored in the implanted CI subjects. Results: 39 of 40 planned subjects were included in this study. In 22 subjects, a positive LA-TT-EABR was recorded. In 11 subjects, the response was insecure. In 6 subjects, no response was recorded. One subject was excluded because of pain during the paracentesis. Among them, 19 were implanted with a CI, and 18 had hearing sensations with a hearing prosthesis post-operative. The sensitivity and specificity of LA-TTEABR in estimating the excitability of the auditory nerve preoperatively are both 100%. Conclusion: LA-TT-EABR was shown as a reliable pre-operative test to objectively evaluate the auditory brainstem response. In addition to LA-TT-EABR, an analysis of the auditory cortex using LA-TT-EALR may provide correlation and confirmation of LA-TT-EABR results and additional information about cortical reorganization after long deafness.