Volume 10, Issue 4 - Proceedings of the 58th Annual Meeting of the German Society of Biomedical Engineering (18 – 20 September 2024, Stuttgart)

Stroke patients with hemiparesis often face significant challenges in accessing consistent and effective rehabilitation therapy due to limitations in mobility and the availability of specialized care. To address this, a new telerehabilitation system is introduced for stroke patients with hemiparesis. It consists of two internetconnected robots for remote wrist rehabilitation. A cloudbased website serves as a centralized interface for patients and doctors, allowing remote control of the system during therapy sessions. To enhance the haptic feedback and stability of the system, a torque/velocity control algorithm is implemented. These control algorithms incorporate damping and spring terms from an impedance control algorithm to synchronize and maintain the positions of both end effectors. Additionally, a wrist evaluation model has been introduced to calculate stiffness and damping coefficients, essential for monitoring patient progress and improving treatment efficacy. The wrist model is evaluated using multiple linear regression method. Experimental testing demonstrates the potential of the proposed approach in enhancing telerehabilitation system performance.

Autism Spectrum Disorder (ASD) presents neurodevelopmental challenges in social interaction, communication, and restricted or repetitive patterns of behaviour. Common conditions of ASD may include difficulties in understanding social cues and engaging in conversations. With the spread of virtual and augmented reality technologies, there has been growing interest to develop game-based learning approaches for helping patients and reducing the impact of ASD. Thus, a virtual environment has been developed to be used in a system dedicated for the emotional training of patients with ASD to improve social interaction and develop appropriate emotional responses to real-life scenarios.

Joint diseases are one of the significant challenges that come with age. The pain that these diseases cause can affect the quality of life of the patient, especially if the disease is causing aches in a high-load-bearing joint like the knee. Most patients these days choose to undergo total joint arthroplasty. However, some patients cannot do the surgery for different reasons or choose to delay it as late as possible. Their only option is to use a walking stick with the associated restrictions in terms of quality of life and incorrect physical strain. To help those patients and reduce their pain, this work aimed to create a concept that reduces the load acting on the knee and lower leg. Alleviating the load on the knee joint has been identified as a crucial factor in reducing pain associated with various knee conditions. To achieve this, the load should be partially transferred from the upper leg to the ground. Following the primary design process stages, a first demonstrator was built and tested. The results of the tests indicated that the device could reduce 30 % of the load exerted on the knee during the first phase of walking. Moreover, it takes around 27 seconds to fasten the device to the user's leg. The overall conclusion is that the first demonstrator proved the validity of the main concept, although there is room for improvement.

The increased use of face masks for infection prevention has resulted in difficulties in speech understanding. Thus, there is a need for tuning of acoustic plans in hearing aids to compensate for the loss of acoustic information due to the mask.Previous studies focused on isolated vowel sounds. Our study measured the effect of surgery masks on the acoustic attributes of vowels and compared the effect of isolated to co-articulated vowels. The voices of 45 male speakers were recorded whilst pronouncing isolated vowels and vowels within the co-articulation in a sentence. All recordings were repeated with the participants wearing a surgical mask. Acoustic factors of length, intensity, pitch, formants, shimmer, and jitter were extracted from the vowels in all the different conditions. The differences between masked and unmasked acoustics in the co-articulated vowels were found to be smaller than the isolated vowels acoustic features. These preliminary results improve the quantification of the effect of hearing a face mask in the real-life production of speech.

Catheter-associated urinary tract infections are among the most common hospital-acquired infections and all previous measures to reduce their incidence have been less successful. Therefore, the present study investigates an approach based on the antimicrobial effect of visible light. The radiation of a violet or blue LED is guided via optical fiber with a diffuser tip into a catheter surrounded by an E. coli suspension (phosphate buffered saline or urine) in a simple urinary tract model. The irradiation led to bacterial reductions of up to several orders of magnitude that depended on various parameters. Violet light had a stronger antimicrobial effect than blue light, even in the experiments with urine, which absorbs violet light more strongly than blue light. An increase in irradiation intensity led to a greater reduction in bacteria. Human cells are not expected to be damaged by this irradiation. Therefore, the application of violet light to prevent catheter-associated urinary tract infections seems particularly promising but should be tested on even more realistic models in the future, which should not only include planktonic cells but also biofilms.

Alzheimer’s disease (AD) is a progressive neurodegenerative brain disorder that primarily affects elderly individuals. Mild cognitive impairment (MCI) represents an intermediate stage between normal cognitive functioning and the onset of AD. During this transition state, various subanatomic structures of the brain, including fornix, undergo pathological changes. In this study, an attempt has been made to analyse the structural variations in the fornix region of MCI and AD using invariant moments. For this purpose, T1- weighted brain structural magnetic resonance (sMR) images are obtained from a public database. The pre-processing of the raw images is conducted in FreeSurfer, and the fornix region is segmented using the reaction-diffusion level set (RDLS) method. Further, seven Hu’s invariant moments are extracted from the segmented fornix region, and statistical analysis is carried out using student t-test and Wilcoxon’s rank sum test to identify the significant features that could differentiate between the MCI and AD conditions. The results demonstrate that the combination of FreeSurfer and RDLS technique effectively pre-processes the brain sMR images and accurately delineates the fornix region. Statistical results revealed that six out of seven invariant moments are significant ( p <0.001). It is observed that the mean values of all the moments in MCI are lower than AD, suggesting a higher degree of structural variation in AD compared to MCI. Considering the potential of fornix alterations in predicting the early stages of AD, the proposed approach holds considerable clinical relevance for further investigation.

Laser-scanning confocal microscopy of the human cornea acquires in vivo images of corneal tissues with cellular resolution, which offers diagnostic potential for a variety of diseases. However, involuntary fixational eye movements induce motion artefacts that pose a challenge for accurate morphometric analysis and particularly for preceding image data fusion steps. Different image registration algorithms promise to eliminate such artefacts, but objective, quantitative evaluation of the registration results is difficult because of the lack of ground truth data. Simulation approaches offer a possibility to close this gap. Existing open-source software for the simulation of fixational eye movements is currently limited to creating drift movement. The present contribution proposes complementary models for microsaccade and tremor components to provide complete simulated fixational eye movement paths. In addition, it reports results from an extensive examination of different model parameter combinations. It is shown that the microsaccade model is capable of reproducing intermicrosaccade intervals that closely resemble experimental data. A software implementation is provided as open-source Python modules.

Prediction of cell painting facilitates highthroughput screening of cellular biology and disease mechanisms, accelerating the discovery of novel therapeutic targets or drugs. This study explores the prediction of cell painted Endoplasmic Reticulum (ER) images from transmitted light brightfield images employing deep learning techniques. A conditional Generative Adversarial Network (cGAN) based framework incorporating Wasserstein loss and Gradient Penalty with a modified UNet++ based generator and a patch discriminator is used to predict cell-painting images from brightfield images captured at varying focal planes. Evaluation of the GAN network reveals promising results with a Mean Absolute Error (MAE) of 0.20, Multi-Scale Structural Similarity Index (MS-SSIM) of 0.80, and Peak Signal-to- Noise Ratio (PSNR) of 56.52. The results showcase the effectiveness of the proposed approach in accurately predicting the ER cell painting channel from transmitted light microscopy. Consequently, the study proposes the ER-WGAN network for predicting cell painting using brightfield images.

Transcutaneous spinal cord stimulation (tSCS) offers non-invasive relief for chronic pain and improves motor function in spinal cord injured (SCI) patients. However, its mechanisms are not currently fully understood, and patientspecific factors, such as Body mass index (BMI) and age, complicate treatments. This paper aims to understand tSCS better by developing a novel Finite Element Model (FEM) of the human body using CT scans. Three subjects (sex, male, female, male. Age: 26, 27, 64. BMI: 38.9, 24.1, 28.4) underwent a CT scan, performed on a Cannon Aquilion Prime (Slice thickness [mm]: 0.8, Voxel size [mm 3 ]: 0.564, 0.328, 0.527), which imaged the trunk of the body, from top of the abdomen to the bottom of the pelvis. The images were then used in Materialize Mimics Research 21.0 to create 3D images of individual organs, skin, fat, muscles, skeleton, and spinal cord. After pre-processing in Autodesk Meshmixer, the models were converted into solid CAD objects in Ansys SpaceClaim R2021 and combined into a single abdominal model. Ansys Maxwell R2021 was then used for simulations. Five different two-electrode configurations were tested in the prototype phase with a simplified model setup. The positive electrodes were placed over the (Thoracic) T10, T12, (Lumbar) L2 and L4 vertebrae sequentially, with the negative electrode over (Sacral) S2. The simulations took on average 47.4 hours. A marked decline in electrical current penetration depth was observed as the electrodes were placed closer together which was consistent with known current distribution patterns. A preliminary validation test was also performed using a lamb’s thigh. Two electrodes were placed a known distance apart and a stimulation was given, needle electrodes were then inserted in a grid-like pattern to obtain voltage values. The setup was recreated and simulated in Ansys Maxwell. The resulting average percent difference was 44.93 ± 33.3 % (5Vpp Square wave) and 35.02 ± 23.38 % (5V DC). In both instances, the highest difference was at the edges of the electrodes and the lowest difference in the midpoint between electrodes. Later versions of FEM models incorporated more organs and had improved on previous mesh generation flaws, but encountered new mesh generation errors which could not be rectified before the conclusion of the master’s project. Despite complications, this project has provided a pipeline for creating similar models and shown their usability. Future work will involve overcoming the current mesh generation errors, reducing calculation time, and performing a thorough validation test.

Inpatient care facilities globally are facing a critical shortage of staff, posing significant challenges to resident well-being and care quality. This issue is further compounded by demographic shifts and increasing care demands. While technological advancements offer promise in alleviating nursing staff burdens, their effective integration remains complex, with nursing staff acceptance playing a pivotal role. This paper describes a systematic approach designed to streamline the process of identifying, categorizing, and prioritizing suitable technologies in inpatient care settings. By taking into account the specific needs and requirements of nursing staff, this approach, validated through a comprehensive case study, aims to facilitate targeted technology adoption, thereby contributing to the successful digitization of this occupational domain.

In the realm of laparoscopic procedures, intelligent context-aware assistance systems hold promise for enhancing surgical workflows and patient safety. This study employs a multi-view approach to recognize surgical phases, combining data from a laparoscopic camera and an in-room camera simultaneously. The study aimed to improve phase recognition accuracy using a Transformer-based model with late sensor fusion, which yielded mixed results. The data poses significant challenges, as self-recorded videos are insufficient for extracting relevant information, necessitating real-world data. Additionally, the overall model needs refinement, as certain components degrade performance with poor data. This research highlights the complexities and opportunities in integrating multiview data for surgical phase recognition, emphasizing the importance of diverse data collection strategies and model architectures for real-world surgical settings.

Emotion influences the daily activity of human life. The complex interaction between the central nervous system (CNS) and peripheral nervous system (PNS) contributes to emotional experiences. Various studies have investigated this interaction during sleep, meditation, deception, and cognition. However, research focusing exclusively on emotion-related interactions is limited. In this work an attempt has been made to assess the CNS and PNS interaction by analyzing Electroencephalogram (EEG) and Photoplethysmogram (PPG) signals during emotional arousal induced by audio-visual stimuli obtained from the DEAP database . EEG signals are divided into four frequency bands: theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (30-45 Hz). The envelope of EEG and PPG signals is then computed to determine cross-frequency coherence (CFC). TheWilcoxon Rank-sum test is employed to assess the statistical significance of CFC in low (LA) vs. higharousal( HA) for various electrodes. Results indicate that CFC can discriminate the LA vs HA. Higher CFC is found in HA compared to LA for the beta and gamma bands, while the opposite trend is observed in the theta and alpha bands. The FP1, FC1, and T7 are found to be statistically significant (p < 0.05) in differentiating LA with HA. Therefore, this study offers insights into CNS-PNS interaction during emotional arousal.

In the bachelor program Medical Engineering, we have introduced the elective course Safety of Medical Electrical Equipment Lab Exercises. One of the lab exercises deals with the topic characteristics of different earthing systems and electricity supply networks in medical used areas. A laboratory setup has been developed and introduced for this lab exercise. With this setup it is possible to evaluate the characteristics of different earthing systems with extra-low voltage in order to avoid hazardous situations.

Paralympic cross-country skiing is a competitive and physically demanding sport developed for individuals with physical disabilities. In addition to good training and endurance, sports equipment is a key factor in achieving success. The design of sports equipment must be customized to accommodate specific impairments. Furthermore, biomechanical and neurophysiological factors need to be considered when designing equipment such as ski sledges. Among other neurophysiological factors, muscle activity, typically measured using electromyography (EMG), plays a crucial role. However, due to the high level of dynamic movement in the sport, EMG measurements are not always feasible. This study explores the possibility of estimating EMG data using neural networks and acceleration data. A feedforward neural network model was created and trained to predict upper body muscle activation from acceleration data. Validation of the model using statistical metrics yielded promising results, suggesting its effective use in predicting muscle activity. This research sets the stage for enhancing understanding and optimizing equipment in Paralympic cross-country skiing, ultimately enhancing the performance of para-athletes.

Electrical Impedance Tomography (EIT) is a medical imaging technique that is primarily used to monitor the respiration of a patient. Because EIT is based on electrical measurements, it is a safe, non-invasive, and cost-effective imaging technique. However, the EIT image reconstruction is a severely ill-posed problem that gives low spatial resolution where only large variations in tissue conductivity can be visualized. Furthermore, widely used time difference EIT relies on a single frequency alternating current measurement which does not allow for discrimination of different tissues on their conductivity spectra. Here we show the application of a new EIT reconstruction algorithm which correlates measurements taken at different frequencies to include the spectral dependency of the tissue properties. The algorithm is tested on a simulated phantom using data for muscle and lung tissue from the literature. It shows that contrary to a standard EIT image reconstruction, the frequency dependence of the tissues is retained, which can be used to further improve distinguishability in EIT images.

This paper presents a comparison of the blood pressure estimation based on Ballistocardiography (BCG) measurements, acquired at two different body locations. The first BCG signal being the proximally measured BCG at the carotid artery, the second being the distally measured BCG at the foot. Parallel to the BCG measurement, the reference blood pressure is measured. Visual inspection of both BCG signals shows a decreased signal quality for the distal measurement. Moreover, in order to estimate the blood pressure based on BCG derived features for beat-to-beat intervals two neural networks are deployed. It is shown that Mean Absolut Error (MAE) is twice as large for the distal blood pressure estimation as for the proximal estimation and that the estimated pressure values of the proximal model show less variation when comparing the reference pressure and estimated pressure visually.

A novel tool to create artificial, cell-based model systems of biological barriers for preclinical drug testing and basic research are artificial collagen membranes (ACM). They closely reflect in vivo basement membrane properties like permeability and stiffness. Besides, they act as initial artificial basement membrane during cell culture. Nevertheless, up to now only complex protocols are published to create such artificial membranes. Also the deflection relies on very low pressure to elongate ACM, if in vivo like deflection - as in the lung mucosa - shall be emulated. Moreover, online monitoring of the deflection of ACM is a crucial part, hence collagen is non-transparent in the visual spectrum, reducing the applicability of standard microscopy. Here we show an easy protocol to generate ACM using commercially available NetwellTM inserts and collagen gel based coating. To address above mentioned issues in membrane deflection we developed a novel pneumatic actuation device (MPSstimulus) as well as a 3D-printed lid and a holder for 6-well plates as an easy to use microphysiological system (MPS) to stimulate ACM. Furthermore, we characterized the 3D membrane structure and dynamic elongation using optical coherence tomography (OCT). Based on our preliminary results, OCT is a promising technique for high-resolution inspection and elastographic analysis of ACM and adherent cell layers using the novel pneumatic actuation device.

Deep learning models for the classification of electrocardiograms (ECGs) are able to learn disease-specific patterns, but they are rarely implemented in medical practice due to their "black box" nature. Post-hoc explainable artificial intelligence (XAI) methods compute regions of interest (ROI) which are of importance for a model’s decision making. However, it needs to be further analyzed whether a model focuses on the morphological or rhythmical information within the ROIs. We evaluate a pre-trained ResNet for sinus bradycardia (SB) and sinus tachycardia (ST) classification on the PTBXL dataset using the XAI method Integrated Gradients. We compare the confidence of the model predictions to ECG features used by clinicians using correlation analysis. Correlation is highest for RR intervals (SB: 0.44) and atrial as well as ventricular heart rates (ST: 0.51), with the majority exceeding clinical thresholds for both disorders, indicating that the model learned rhythmical features. Except for QT intervals in ST classification, morphological features such as duration and amplitudes of P-/T-waves do not show any correlation.

In Magnetic particle imaging (MPI), the spatial encoding of the particle response is achieved by a magnetic gradient field called selection field. In this work a selection field generator based on two pairs of nested and freely rotatable Halbach cylinders in dipole configuration generating a gradient field in form of a linear field free region (FFR) is proposed. Through adequate rotation of the Halbach dipoles the FFR can be moved continuously along the cylinder axis. To understand and evaluate the movement of the FFR for different sequences of dipole rotations a simulation study is conducted. The simulation is based on the approximation of a permanent magnet’s field as a magnetic dipole field, so that the Halbach cylinder field results from the superposition of circularly arranged magnetic dipole moments. By the summation of four Halbach dipole fields the selection field can be calculated. It has turned out that the opposite rotation and the matching field strengths of the dipoles of a pair are prerequisites for the movement of the FFR. With regard to the gradient field strengths and the movement speed of the FFR within a potential imaging area, a phase-shifted rotation of two pairs seems promising for the use in MPI.

Hyper-viscoelastic models have been used to characterize large strains coupled with viscosity. In this study, we aimed to model the urethral in-vivo biomechanics through a hyper-viscoelastic implementation. First, we used isotropic 2-parameter Demiray model for identification of urethral tube inflation after refining the values of quasistatic states. Attempting the approach by Holzapfel et. al., the average ratio of the elastic and dynamic modulus was computed to determine the stress contribution of the viscoelastic branches. The values of the parameters were determined after constraining a constant Energy dissipation (generalized maxwell) over range 1s to 100s. For the hyper-viscoelastic comparison implementation, it was observed that the goodness of fit criteria performs good for half of the samples (Adjusted R2>0.95). In some samples, the model is limited to fit ‘S’ shape curves but still performed well. The above identification technique and the hyper-viscoelastic in-silico approaches show that our approach fares sufficiently for the creep response characterization of the urethra.

Distortions in laparoscopic videos affect surgeon visibility and surgical precision, underscoring the need for sustained high video quality. This study presents a real-time laparoscopic video quality assessment algorithm independent of reference content availability. Statistical parameters derived from luminance, local binary pattern and motion-vector maps of video frames are observed to effectively discern distortion types and severities. These parameters are used to train an evolutionary adaptive neuro-fuzzy inference system (ANFIS) end-to-end with subjective score labels. Training and validation loss curves saturate at the 85 th epoch, demonstrating the model’s efficient data fitting capability. Performance comparison with other state-of-the-art methods reveals superior results, with high correlation scores of 0.9989 and 0.9446 for experts and 0.9956 and 0.9847 for non-experts, alongside low root mean square errors of 0.0828 and 0.1685 for expert and non-experts, respectively. The model accurately replicates the expert and non-expert perceptual opinions, encouraging future research in stereoscopic, augmented, and virtual reality data.

The corrosion behavior of platinum electrodes in cochlear implant (CI) systems is a critical factor affecting their long-term functionality. This study investigated the influence of the surface roughness of the platinum electrodes on their corrosion behavior. The results of in vitro experiments indicate that higher surface roughness tends to accelerate corrosion by providing numerous initiation sites. Although the electrochemical measurements of platinum electrodes with different surface roughnesses showed only slight differences in terms of shifts in potentials and higher corrosion current densities, data from the literature suggests an enhanced charge capacity with rougher surfaces. These findings contribute to an understanding of the failure mechanisms of CIs and can ultimately help to improve the design and durability of CI systems.

People worldwide suffer from bladder incontinence. For patients it is difficult to train the pelvic floor without a physiotherapist. The aim of this paper is to show how smart textiles can be applied in telemedicine. Textile electrodes and conductive paths are embroidered into underwear. The electromyography (EMG) signals of the pants are sent to an app. The app visualizes the data of the muscle activity.

This study examines hydrophobic surface coatings for stimulation electrodes to mitigate foreign body reactions and encapsulation. Electrospinning of polydimethylsiloxane (PDMS) resin is explored to create fiber structures for electrode coatings. PDMS resin is succesfully processed into fiber mats. Electrospun PDMS fiber structures showed an increased water contact angle (124 °/125 °) in comparison to a cast flat film (102 °). To further increase hydrophobicity, the fabrication process of Slippery Liquid-Infused Porous Surface (SLIPS) systems with a polyurethane substrate and different natural lubricants is assessed with promising results in an increase in water contact angles up to 11°. The findings suggest the potential of these surface coatings to enhance the performance and safety of stimulation electrodes.

Fatigue behaviour is a crucial aspect of mandatory mechanical tests for regulatory purposes, aimed at determining the load at which the bone plate withstands under a specific number of cycles, known as the runout condition. However, current test standards, such as ASTM F382, provide setup configurations without explicit guidelines on required fatigue strength. The determination of the minimum level of in vivo performance that the plate must fulfil remains an open issue, which is frequently addressed by the direct comparison with predicate devices. To address this gap, this study proposes an in silico framework to estimate maximum stress on implanted femoral plates for comparison with four-point bending tests described in the ASTM standard, deriving appropriate safety factors. As case studies, three femoral plates were assessed, and results showed safety factors above 1.1, indicating the reliability of the implanted plates.

Transcatheter aortic valve replacement (TAVR) has become the standard treatment of multimorbid patients with severe aortic valve stenosis. One frequently observed complication after TAVR procedure is the occurence of paravalvular leakage (PVL). PVL is caused by a gap between the vessel wall and the TAVR stent, enabling blood to flow from the aorta back to the ventricle during diastolic phase. A high PVL rate leads to increased mortality of TAVR patients. To avoid this complication, pericardial skirts were developed by manufacturers to seal the leakage gap. Furthermore, the aim is to improve the conforming expansion of the TAVR stent to the vessel wall, especially for the self-expanding TAVR consisting of nitinol. One factor for the expansion is the size and shape of the TAVR stent cells, which vary between 9 and 15 cells in the circumferential direction for established TAVR devices. To quantify the impact of different cell sizes on the occurence of PVL, we performed numerical studies with different TAVR designs and investigated the PVL for each TAVR design. For this purpose, we developed three different TAVR designs with 9, 12 and 15 stent cells in the circumferential direction of the TAVR. These were deployed into a generic aortic root model with standardized calcification in the annulus region using finite element simulations. Afterwards, the PVL was calculated using numerical flow simulations. We found that the TAVR stent design with nine cells had the highest PVL rate (11.2mLs −1 ). In contrast, the TAVR stent design with 15 cells had the lowest PVL (4.8mLs −1 ). Our study thus showed a decisive impact of cell size on the sealing behavior of the TAVR stent and, accordingly, the PVL.

The bending properties of catheters and stents have a big influence on their success during clinical application. Whereas bending stiffness directly affects the trackability of catheters, stents underlie bending when used for renal, superficial femoral or femoro-popliteal arteries, respectively. A three-point bending test setup according to ASTM F2606 was implemented into a universal testing machine and was validated regarding influence parameters such as accuracy of force measurement, test speed and span length. The bending stiffness can be calculated from the initial linear portion of the force-deflection curve. Furthermore, a multi-cycle approach was developed to evaluate the bending properties at large deformations revealing the deflection depending peak load as well as deflection depending elastic recovery rate after unloading. Within this study samples with a length of 120 mm from the proximal region of a commercial coronary balloon catheter were used. Influence of test speed was not significant. However, bending stiffness increased with increasing span length. For multi-cycle measurements a constant test speed and constant span length was used. Test results showed an increasing plastic deformation and peak load as well as a decreasing elastic recovery rate as expected. The presented test method can be used for catheters as well as stents with a length diameter ratio of at least 4:1.

High intensity focused ultrasound (HIFU) is a noninvasive treatment technique to induce thermal or mechanical bioeffects. Characterising the wave field is essential for reliable and reproducible transducer operation in clinical use. This paper presents a schlieren technique to visualise and quantify transducer wave fields. The technique is based on the piezo-optical effect of water, i.e. the refractive index variation caused by sound pressure. Our custom-built system equipped with a Raspberry Pi HQ camera can capture schlieren photographs of acoustic fields generated by a clinical HIFU system. Alternatively, a high-speed camera allows analysis of short burst pulses. We investigated the focal zone shape of continuously generated HIFU fields at acoustic powers of 10- 250 W. Images of the focal area at 100 W indicated dimensions comparable to the reported literature values. Notably, as power increases, we observe waveform distortion in the focal zone due to nonlinear propagation of ultrasound. Our findings demonstrate the efficacy of our system in visualizing and characterizing the acoustic fields generated by a clinical HIFU device.

Optogenetics enables insights into the development of neural diseases. Custom-designed neural probes are necessary to access targeted brain regions. Multifunctional and mechanically flexible probes with minimal footprint, comprising fluidic channels for drug delivery, light sources for optical stimulation, and micro electrodes for electro-physiological readout are beneficial due to reduced tissue trauma and consequently increased long-term stability. This study introduces a mechanically compliant neural probe that provides microfluidic channels and electrodes arranged close to fluidic outlet ports. The key challenge is to open the fluidic inlet and outlet ports avoiding potential channel clogging. A novel laserbased patterning process is demonstrated using a 248 nm KrF excimer laser. It offers high design flexibility in positioning the fluidic ports along the fluidic channels, as well their contamination-free opening under dry conditions. The laser patterning process applies reflective metal layers inside the fluidic channels, confining the laser ablation to the channel cover, thus minimizing potential damage of the channel substrate. Based on its high reflectivity and correspondingly low absorption and transmission at the applied laser wavelength, aluminium is determined to be the best choice for this protective layer.

Core-shell capsules (CSC) are a promising approach for 3D cell culture because they overcome the challenges of traditional large-scale cell cultivation techniques used in tissue engineering. Currently, CSC are segmented from microscopic images in a cumbersome manual procedure to evaluate their properties, such as size or complete encapsulation of the core compartment. In this paper, we propose an automated segmentation process of CSC based on an unmodified YOLOv8 instance segmentation model.We train the model exclusively on synthetic CSC images created from 10 manually annotated real images and evaluate its performance using the common Intersection over Union (IoU) metric on a test set consisting of 181 real images. Without modifying the model or tuning the hyperparameters, we achieve a mean IoU of 0.86, underlining the potential of deep-learning-based CSC segmentation relying entirely on synthetic training data.

This contribution examines the concept of circular economy (CE) within the healthcare sector, introducing the 9R paradigm as a novel framework for the implementation of sustainable strategies. Given the significant CO2 emissions from the medical device sector and the heterogeneous digitalization across the industry, CE offers an avenue to minimize environmental impact while simultaneously enhancing patient care. Leveraging advanced data platforms and digital technology can help reduce resource waste and CO2 emissions, while also optimizing patient care by avoiding redundant diagnostics and treatments. The contribution provides a brief introduction for the application of the 9R paradigm in the healthcare sector and highlights the opportunities associated with the implementation of CE strategies. It outlines innovative data-driven business models based on CE and digital transformation principles. In conclusion, the strategic application of R-strategies can be described as a multi-layered approach requiring close collaboration among all stakeholders to shape a more sustainable and efficient future in healthcare and provide new digital business models.

Antimicrobial surfaces are a promising option for supporting hygiene measures. In addition to many different technologies, research is also ongoing into coatings that are modelled on natural surfaces. In an international project with the National Nanotechnology Laboratory in Costa Rica, the Technical University of Applied Sciences Amberg-Weiden is working on such an aluminum-based nanostructured coating, which is modelled on insect wings and has an antibacterial effect in nature. The aim is to test the effectiveness of these surfaces against bacterial pathogens (Escherichia coli and Staphylococcus aureus) in a standard test procedure, as well as to investigate the resistance of the nanostructures to abrasion and reprocessing. Since the effect of such surfaces depends on the filigree nanostructure, resistance to possible abrasion or damage is a key factor here. Our results show that these coatings exhibit at least detectable activity (log 2.2) against the bacteria in the ISO 22196 test setup. Even after repeated wiping of the coatings with sterile water or reprocessing using ultrasound and wipe disinfection, no loss of effectiveness can be detected. Due to the intrinsic antimicrobial effect of the aluminum, the effectiveness of the nanostructured surface is even higher compared to a completely ineffective control surface. If further studies confirm these initial positive results, the coating we investigated could represent another effective alternative in infection control and prevention.

In this study a method for determining the best placement and optimal orientation of the implantable piezoelectric accelerometer attached to the short process of the incus is developed. The accelerometer is intended to be used as a replacement for an external microphone to enable totally implantable cochlea implants or as part of a totally implantable hearing aid. The best location is determined to be near the incudomalleolar joint. The optimal orientation of the sensor at the determined location is obtained using two criteria - maximum voltage sum and minimum loudness level sum. The method described in the paper can be used to further optimize the design and the performance of the accelerometer.

Finite Element Models (FEM) for simulating various physical phenomena are indispensable in today’s research. They are also applied in high-frequency (HF) surgical applications to simulate Joule heating and thermal necrosis formation in biological tissue. One challenge lies in choosing an appropriate mesh size, balancing between sufficiently accurate simulation results and a coarse mesh for fast computations. In this contribution, we investigate, using three mesh sizes (coarse, medium, and fine), the extent to which regression analysis of discrete simulation results of necrosis dimensions can compensate for mesh refinement in an FEM for HF soft coagulation. We conducted exponential regression analysis on discrete simulation results of the necrosis zone. It has been found that while it is generally possible, if the FEM simulation results already deviate significantly from the true solution, regression analysis cannot compensate for errors arising from the numerical method. Therefore, a coarse mesh with subsequent regression analysis can be utilized to determine necrosis propagation in depth, with a maximum error of only -3.4 % compared to the fine mesh within the considered interval. However, for the necrosis in the lateral direction, the maximum error is 44.3 %, which is too large. Even the mid-size mesh is not accurate enough in the lateral direction, necessitating the use of a fine mesh for the lateral direction.

A bubble collapsing near an interface may result in the formation of a liquid jet protruding from the distal bubble side, through the bubble, towards the interface. Ultrasound assisted jetting has been observed when subjecting, by approximation, infinite fluids to acoustic amplitudes above the inertial cavitation threshold, limiting the possibility of ultrasoundguided, bubble-assisted drug or gene delivery. However, the vascular system can be regarded as a finite fluid. The purpose of this study was to investigate the feasibility of lowamplitude jetting for fluid containing biocompatible cavitation nuclei, by placing the region of interest in a confined space to ensure a standing wave field. Droplets of Quantison TM ultrasound contrast agent were pipetted into a Perspex cylindrical compartment of 8-mm diameter and 2-mm height, which was part of an imaging system. The contrast agent was subjected to 3-cycle ultrasound pulses with a centre frequency of 1MHz whilst being observed at a frame rate of ten million frames per second. Jetting was observed to occur with microbubbles nucleated from the contrast agent in an acoustic regime whose free-field mechanical index was 0.6. Empirical curve matching showed a pulse amplification by a factor of six owing to the chosen geometry. Visible jet lengths of twice the bubble radius on the verge of collapse were measured. Owing to the confined space, the local acoustic amplitude was amplified to surpass the cavitation threshold. This finding is of interest for medical ultrasonic applications where the local environment comprises reflectors.

The development and discovery of new drugs and therapy products in regenerative medicine are carried out through multiple stages which involve several strict quality protocols. The use of cell-based model systems is the most relevant alternative to deviate from animal testing. However, the underlying biomanufacturing process in a 3D environment can be prone to failures or unexpected behaviors such as cell growth anomalies or inconsistencies in cell aggregation, which can seriously affect the quality of the result. Therefore, it is necessary to establish a non-invasive and continuous monitoring mechanism that minimizes contamination risks, allows taking early decisions and enables large-scale production in the long term. The results of our embedded machine learning approach show that it is feasible to train machine learning models that can operate on a resourcelimited embedded board with acceptable prediction accuracy for the estimation of the average size of growing stem cell spheroids. Further experiments are needed to investigate the full information potential of the recorded process data.

The analysis of spontaneous mind wandering is crucial for comprehending an individual mental state and holds the potential to enhance performance and productivity. This paper proposes a framework using Continuous Wavelet Transform (CWT) based ResNet model to analyze Electrodermal Activity (EDA) signals for mind wandering detection. In this analysis, EDA signals are sourced from an openly accessible database and preprocessed for artifact and noise removal. Time-frequency analysis generates CWT spectrogram images, which are classified using a modified ResNet50 model that is custom built to classify the spectrogram images corresponding the mind wandering and awareness. Hyperparameter tuning is carried to obtain the optimal network parameters that provides the best accuracy. Results indicate that batch size of 32, learning rate 1e-5 provides better results. This hyperparameter tuned model achieved an accuracy of 64% in differentiating between the two classes. This paper proposes an adapted ResNet50 model that could be employed in wearable devices as a potential application of knowing the mind awareness of an individual.

The apnea test (AT) is a critical procedure in determining brain death. It temporarily disconnects the patient from the ventilator for 7 to 10 minutes. This study explores pulsatile Electrical Impedance Tomography (EIT) during AT in 10 patients suspected of brain death to discern physiological changes. Analysis revealed three distinct patterns of pulsatile EIT signal changes: increasing; fluctuating; and decreasing. These findings show the potential of pulsatile EIT monitoring to provide novel insight into cardio-pulmonary dynamics during AT.

Despite the wide variety of knitting patterns present in the global market for synthetic meshes, the influence of mesh morphology on the mechanical behaviour of these devices is not yet clear. The goal of this project is to determine how the mechanical properties of monofilament polypropylene warp-knitted meshes are influenced by their morphological characteristics. Multivariate linear regression models were developed using nine meshes to quantify a correlation between these two crucial aspects. Additionally, porous mesoscale Finite Element (FE) models were implemented in Abaqus to replicate in vitro tests conducted in a previous study. High values of the determination coefficient were obtained for all the models and their robustness was confirmed by good performance in a leave one out validation method. The porous FE models successfully replicated the multidirectional behaviour of the meshes after the calibration of the elastic modulus based on the uniaxial tensile test. This combined approach demonstrates valuable results paving the way for advancements in hernia treatment and meshes design.

A novel two-phase aerosolisation mode utilizing porous materials is investigated, aiming to improve aerosol delivery for medical inhalation. Sintered stainless steel filters with varied pore sizes (PS) from 0.2 μm to 7 μm were used to generate aerosols from a 0.9 wt.% sodium chloride solution. Droplet sizes and delivery rates were measured using laser diffraction spectroscopy. Further measurements included shadow imaging. Results indicate that aerosolisation occurs within a specific range of PS with droplet sizes increasing with increasing PS. The droplets generated are suitable for inhalation therapies. A hypothesis is established about the process of droplet formation which states that different PS within the porous material serve distinct functions that contribute to the breakup of liquid films into aerosol particles. Droplet formation is the result of film breakup in pores filled with fluid. This low-energy aerosolisation method has the potential to be used in handheld devices for sensitive drug formulations, overcoming the limitations of current technologies. Further research is needed to optimize the pore size distribution and enhance aerosol generation efficiency.

Mathematical patient models play a profound role in the managing and understanding of anaesthesia. This article proposes a patient model for simulating anaesthetic uptake, i.e. the storing of anaesthetic gases in the human body, thus extending a well known four compartment model from literature. Real time data is used for the simulation, from which the sevoflurane concentration is extracted. The exhaled concentrations from the model and the data are compared. A literature model is extended with ratios of compartmental perfusion, the introduction of coefficients representing diffusion and the change of the solubility coefficient. The extended and the literature model are applied to five training data sets, with prior optimization of the parameters that have the most impact. Afterwards, both models are validated on five additional data sets. The results of the extended model are mostly better than the results of the literature model. Therefore, the extensions improve the model for the used data, but need to be validated with a larger amount of data.

Functionality of state-of-the-art medical devices often depends on high-end materials. Today, a large spectrum of medical devices like heart valves, vascular grafts, sutures or endoscopic devices consist of or incorporate fluoropolymers like polytetrafluoroethylene (PTFE). Fluoropolymers offer unique mechanical, electrical and chemical properties. Based on their chemical constitution, fluoropolymers are assigned to the group of per- and polyfluorinated substances (PFAS). The aim of the current restriction proposal within Europe (EU) is to limit and/or fully ban the persistent PFAS substances known as forever chemicals. The restriction proposal concerns all PFAS substances beyond further subdividing and evaluating their hazardous potential and with just short-term derogations depending on the application. The consequences of the impending restriction proposal on PFAS is shown from a medical device industry viewpoint. Literature data on annual material consumption and supply chain are collected and evaluated, especially pointing to future problems assuming an entry into force of the pending restriction proposal. Furthermore, the short derogation times are contextualized with current timelines of Medical Device Regulation (MDR) approval processes to highlight the impact on product availability assuming a PFAS ban. Selected examples of affected products illustrate, how patient treatment options are limited by the PFAS restriction. It can be clearly shown that the PFAS restriction will critically affect the medical device sector through collapsed supply chain and insufficient derogation times for product approvals, thus limiting the availability of medical products and setting back the gold standards in medical treatment.

The interconnection of medical devices in an operating room (OR) represents a major step in optimizing clinical processes and increasing the quality of treatment. The IEEE 11073 Service-oriented Device Connectivity (SDC) standard family constitutes the foundation for manufacturerindependent information exchange and remote control of medical devices. However, integrating new SDC-capable devices into an existing OR network poses a major challenge for medical device manufacturers. Thus, suitable integration models are required. This work focuses on the definition of three possible integration models and their comparison according to architectural design patterns. Thereby, the use case of integrating a high-frequency (HF) surgical device to interconnect with existing SDC-capable devices is pursued. One of the models, which focuses on high expandability and low coupling, was successfully applied to interconnect an HF surgical device with an OR light in the research OR of Reutlingen University. The results indicate transferability to other integration scenarios and are intended to further promote manufacturer-independent integrated ORs.

Involuntary childlessness represents a sensitive and emotionally moving topic. Depending on the method, reproductive treatments are associated with significant physical, psychological and financial burdens. Female sterility is caused by Fallopian tube occlusions in one of three cases. Our previously described microstent technology opens up new therapeutic possibilities to restore the lumen of the Fallopian tube without surgery. In this work, Finite Element Analysis was performed for design optimization of a polymeric selfexpanding microstent which had a limited crimping diameter by self-contact resulting in challenging loading behavior during application preparation. Three different stent designs were investigated. A bilinear constitutive material model considering isotropic hardening was used for modeling of the microstent. To evaluate the stent variations, the following criteria were defined: self-contact, maximum stresses (von Mises stress) and volume of stress above 45 MPa. All microstent designs were crimped to a diameter of 1.0 mm using Finite Element Analysis. Stent design version 1 and 2 were limited to crimping diameter considering self-contact. The wave-shaped cell design of version 3 completely prevented self-contact up to a diameter of 1.0 mm. Consequently, stent design version 3 offers the greatest potential for a self-expanding polymeric microstent for minimally invasive treatment of Fallopian tube occlusions.

Colonoscopy plays a pivotal role in early colorectal disease detection. It is hindered by procedural difficulties such as friction between colonoscope and colon wall as well as maneuverability issues. Expanding robots offer a promising alternative, aiming for a reduced risk of tissue damage and reduced forces between colononscope and tissue. In this paper, the usage of expanding locomotion for colonoscopy is investigated to address technical challenges. Preliminary tests on a custom-designed test rig demonstrate the potential of intermitted advancement and retraction modes to overcome challenges such as the difference in pathlength of robot body and working channel as well as the typical buckling during retraction of expanding robots. Future research will focus on refining control systems based on sensor feedback and evaluating additional parameters for clinical application.

The European Artificial Intelligence Act (AI Act) has profound implications for technological innovation in the medical and health care sector, transcending the boundaries of existing legal frameworks such as the Medical Device Regulation (MDR) and the General Data Protection Regulation (GDPR). This paper examines basic regulatory choices of the AI Act relevant for the field of digital health innovations by contextualizing its main goals, key obligations, and addressed actors. In light of these considerations, we present a scoping literature review that identifies potential regulatory challenges for stakeholders engaged in research, innovation and healthcare. Building on this, we point to concepts and methodologies to overcome such challenges in a way fostering innovation while realizing key constitutional and societal interests at the same time.

Acoustic or visual warning signals for workers in hazardous situations might fail under loud and/or lowvisibility work situations. A warning system that uses electrocutaneous stimulation can overcome this problem. This study aimed to compare vertical, diagonal, and horizontal current stimulation directions at the upper arm to select the one with the lowest amount of muscle twitching. Fourteen electrodes were attached in two rows to the upper right arm of 15 participants. The stimulation was conducted with bi-phasic rectangular pulses of 150 μs and amplitudes of up to 25 mA. Muscle twitch thresholds have been determined and a circumferential stimulation signal was presented as warning pattern for the three current stimulation directions and evaluated regarding alertness, discomfort, and urgency. For single stimulation pulses, muscle twitches occurred slightly less often at the horizontal stimulation direction compared to the other two and muscle twitch thresholds showed no systematic differences. For the warning patterns, no considerable differences were found regarding the evaluation of alertness, discomfort, and urgency and no differences were found for muscle twitching. In conclusion, all orientations seem suitable for warning pattern presentation and none of the directions has a clear advantage in reducing muscle twitch.

Endophytic fungal extracts possess great potential as alternative antibacterial substances. Here, two extracts from the Australian endemic plant Eremophila longifolia were shown to reduce biomass and metabolic activity of the oral bacteria Streptococcus oralis and Porphyromonas gingivalis. As the effective concentration towards S. oralis was lower than the cytotoxic concentration towards oral epithelial cells, more detailed studies are warranted in the future.

In this cross-sectional study, we examined the abundance of blood glucose meter (BGM) models in the Polish and German medical device markets and their technical specifications. Information from a medical equipment wholesaler’s database was gathered for comparison of BGMs from five manufacturers: Roche Diabetes Care, LifeScan Inc, Abbott, Ascensia Diabetes Care, and Genexo, and for model Glucomaxx BT TaiDoc Technology Corporation. Key parameters such as blood sample size, measurement time, memory capacity, and measurement range were considered. An application was developed to aid in BGM selection for people with type 1 or type 2 diabetes. The results showed a high correlation with laboratory measurements and similarity in key functionalities between the models. The application, tested on 30 individuals aged 18 to 79 years, demonstrated stability, speed, and a user-friendly interface, facilitating efficient data processing and generating recommendations.

Electrospinning has emerged as a versatile technique for producing nanofibers for diverse applications. The fibers produced are highly regarded for their biocompatibility, exceptional surface-to-volume ratios, porosity, and adjustable composition properties, making them promising scaffolds for tissue engineering. In the literature, annatto-loaded cellulose acetate has already been successfully used for electrospinning. However, N,N-dimethylformamide (DMF) was utilized as a solvent in the experiments, which is considered a carcinogenic and mutagenic substance. The aim of this study is to analyze whether comparable results can be achieved with a low toxicity solvent consisting of acetone and dimethyl sulfoxide (DMSO). In this study, we investigate the electrospinning process of cellulose acetate (CA) and cellulose acetate combined with annatto extract (CA/A). Initially, various polymer concentrations ranging from 12% to 16% (w/v) were characterized by rheological measurements to determine their suitability for electrospinning. The morphology and diameter distribution of the electrospun fibers were analyzed using scanning electron microscopy (SEM). Contact angle measurements were carried out to determine the wettability of the electrospun meshes. The rheological investigations conducted revealed that increasing the polymer content of CA raises viscosity, while the incorporation of annatto decreases it. Uniform fibers are achieved at polymer concentrations of 14% and above. The electrospun nanofibers exhibit uniform morphology and diameters in the nanometer range, indicating the successful fabrication of annatto-loaded CA nanofibers. The contact angle measurements showed hydrophilic behavior on all investigated meshes. The incorporation of annatto extract in the electrospun fibers holds promise for various applications, including biomedical scaffolds, scaffolds for cultured meat, filtration membranes and food packaging materials. This study provides valuable insights into optimizing electrospinning parameters for the fabrication of functional nanofibers with enhanced properties.

We investigate the force-frequency relationship (FFR) representation of human ventricular cardiomyocyte models and point out shortcomings, motivated by discrepancies in whole-heart simulations at increased pacing rates. Utilizing the openCARP simulator, simulations across frequencies ranging from 1 Hz to 3 Hz were conducted. Experimental data on healthy human ventricular cardiomyocytes were collected and compared against simulated results. Results show deviations for all models, with Tomek et al. modeling time sensitive biomarkers the best. For example, the ratio of time to peak tension at 2 Hz and 1 Hz is around 85% for experiments, 82% for hybrid data, 95% for Tomek et al., 98% for O’Hara et al. and 138% for ten Tusscher et al. These discrepancies, highlight not only the need for careful selection of ionic models, but also the importance of refining ventricular cardiomyocyte models for advancing in-silico cardiac research.

The technique of PC-MRI flow measurement offers a great opportunity to diagnose and understand the pathogenesis of various diseases and clinical pictures. Although previously criticized for its lack of accuracy, the method is now expanding the possibilities in several research areas. In this study, the design of an MR-compatible pump for the validation of PC-MRI flow measurements is presented. Initial PC-MRI measurements have demonstrated MRcompatibility and the ability to record artifact-free flows. The flow generated by the pump was further measured with an ultrasound flow sensor and compared with a physiological flow curve. The results showed that both the course and the minima and maxima match. Thus, the resulting pump generates a bidirectional, pulsatile flow within a physiological range and a variable frequency between 60-90 rpm with a stroke volume of 0.9 ml. Further flows, frequencies and stroke volumes can be adjusted. This enables the validation of PCMRI flow measurements and addresses current difficulties such as the lack of in vivo data.

This paper shows the possibility of using Ultrawideband (UWB) DW3000 transceivers for noninvasive vital sign monitoring via a transversal propagation method. The transceivers can provide a cheaper, more flexible, and easierto- apply measurement of vital signs than traditional electrocardiogram (ECG) devices. This will not substitute the common medical use but instead can make vital sign monitoring more accessible in other scenarios. Potential applications beyond clinical settings, like measuring the vital signs of a car driver, can be achieved. The primary focus of this study is the possibility of utilizing inexpensive UWB transceivers for ballistocardiography (BCG) measurements. The vital signs are measured on a small focus group to verify the results on a larger spectrum. A photoplethysmogram (PPG) device is used for reference measurement. The results show a great accuracy of 3 breaths per minute for respiration rate measurement. The heartbeat measurement shows an error mostly within 3 bpm due to the resolution of the proposed setup. Methods for further verification of the results, together with increasing the accuracy and resolution of the setup are proposed.

Antifibrotic treatment during glaucoma surgery leads to an improved outcome for patients. Drug delivery systems for placement under the subconjuctiva after surgery made of cyclodextrins and crosslinked with Epichlorohydin were loaded with Josamycin and tested in vivo. After subconjunctival application in three New Zealand White rabbits, blood samples were taken by the means of Mitra devices at defined time points. Determination of blood levels of Josamycin was performed using liquid chromatography mass spectrometry. Detection of Josamycin was achieved during 48 h suggesting a sustained release over the course of this study.

Background: Bladder cancer (BCa) is the second most common type of cancer in the genitourinary system and causes approximately 165,000 deaths each year. The diagnosis of BCa is primarily done through cystoscopy, which involves visually examining the bladder using an endoscope. Currently, white light cystoscopy is considered the most reliable method for diagnosis. However, it can be challenging to detect and diagnose flat, small, or poorly textured lesions. The study explores the performance of deep learning systems (YOLOv7- tiny, RT-DETR18), originally designed for detecting adenomas in colonoscopy images, when retrained and tested with cystoscopy images. The deep neural network used in the study was pre-trained on 35,699 colonoscopy images (some from Mannheim) and both architectures achieved a F1 score of 0.91 on publicly available colonoscopy datasets. Results: When the adenoma-detection network was tested with cystoscopy images from two sources (Ulm and Erlangen), F1 scores ranging from 0.58 to 0.81 were achieved. Subsequently, the networks were retrained using 12,066 cystoscopy images from Mannheim, resulting in improved F1 scores ranging from 0.77 to 0.85. Conclusion: It could be shown that transformer based networks perform slightly better than YOLOv7-tiny networks, but both network types are feasable for lesion detection in the human bladder. The retraining of the network with additional cystoscopy data led to an improvement in the performance of urinary lesion detection. This suggests that it is possible to achieve a domain-shift with the inclusion of appropriate additional data.

Automated laparoscopic video analysis is essential for assisting surgeons during computer aided medical procedures. Nevertheless, it faces challenges due to complex surgical scenes and limited annotated data. Most of the existing methods for classifying surgical tools in laparoscopic surgeries rely on conventional deep learning methods such as convolutional and recurrent neural networks. This paper explores the use of pure self-attention based models-Vision Transformers for classifying both single-label (SL) and multi-label (ML) frames in Laparoscopic surgeries. The proposed SL and ML models were comprehensively evaluated on the Cholec80 surgical workflow dataset using 5-fold cross validation. Experimental results showed an excellent classification performance with a mean average precision mAP=95.8% that outperforms conventional deep learning multi-label models developed in previous studies. Our results open new avenues for further research on the use of deep transformer models for surgical tool detection in modern operating theaters.

Introduction While various models and studies have investigated the simulation and theoretical description of human gait, previous research has not considered irregular situations such as slipping and tripping. However, understanding these situations could be crucial and provide insight into assessing dynamic balance, a factor that is closely linked to the risk of falling. The first step is to investigate the system under normal walking using a participant group that includes people who are particularly susceptible to an increased risk of falling, such as older adults or people with certain health conditions that have so far been neglected in the literature. Methods In this study a control engineering approach was used to estimate the parameters of the human gait system. A total of 42 subjects aged 18 to 78 years, with different body masses and walking speeds, were analyzed. Measurements were conducted on a treadmill with integrated force plates, while participants wore inertial measurement units (IMUs) at lumbar level and were filmed with cameras. Results The calculated spring-mass-damping model parameters, including stiffness (k) and damping (c), varied widely among participants, with stiffness ranging from 4.45 to 45.53 kN/m and damping from 0.26 to 2.13 kNs/m. Contrary to previous findings, no linear relationships were observed between stiffness or damping and velocity, age, or mass. Outliers were identified and related in a plausible way to visual inspection. Conclusion In conclusion, our study successfully characterized human walking during periodic excitation for a diverse participant group using a simple control engineering approach, yielding comparable results to previous studies. Our results pave the way for future investigations to analyze the response of an individual participant to irregular gait perturbations.

At present, there are no bioresorbable systems available for the treatment of aortic coarctation in pediatric patients. However, experience with off-label used systems and knowledge of bioresorbable scaffolds in other applications suggest potential benefits. While stenting techniques are typically preferred for older children, there have been encouraging reports regarding their use in newborns and very young infants recently. Bioresorbable scaffolds could potentially close the technological gap in this area and serve as a bridge therapy for non-operable patients. This work identifies geometric and loading aspects in the light of current literature to improve the growth-responsive development of new scaffolds for the treatment of congenital heart diseases with pediatric-specific characteristics.

Research in the field of artificial intelligence (AI) in medicine is increasingly relying on algorithms based on deep learning (DL), especially for radiology. Despite producing promising results, DL models have a major drawback: their reliance on large training datasets. Especially in medicine, large, annotated datasets are hard to obtain, leading to low robustness and a performance loss when exposed to unseen, new data. To address this problem, our research evaluates how well data augmentation is able to expand the used dataset and thus improve a DL model. We employ 17 different augmentation methods to test the robustness of a DenseNet-121 trained to classify Acute Respiratory Distress Syndrome (ARDS) in chest X-rays. Our experiments show that while the model has low robustness for augmented test data when trained on unaugmented data, the general performance for ARDS classification can be improved by augmenting the training data. Overall, this demonstrates that data augmentation is beneficial in training AI models for ARDS classification in order to create more robust and generalizable models.

It is recognized that teaching and embracing entrepreneurial skillsets lead to more valuable and impactful innovations in biomedical engineering. Unfortunately only very few institutions globally have started to integrate innovation generation methods and entrepreneurial mindset development in their curricula. A novel approach was developed and tested for the IEEE CASS society. A 6 week online training program was developed (30 videos, worksheets, deliverables, and regular tests) and subsequently advertised leading the participants, accompanied by weekly personal guidance, to create a real innovation project. This was embedded in a competition to allow the best teams to receive funding to be able to attend the annual society conference and participate in a competition. The learners all reported a changed mindset towards innovation generation and the presented results were stimulating and exciting. The paper provides details on the free training program and shows some of the results. This could - and maybe should - become a standard training and program event point at future biomedical engineering conferences.

The integration of mechatronic systems to improve the control and maneuverability of endoscopes in complex gastroenterology procedures is necessary since manual handling often lacks precision. The result are flexible endoscopy robots. Therefore, the question arises as to how far these robots have evolved. This basically depends on their degree of autonomy and the typical interventions they are designed to perform. In this review, we provide an overview of existing flexible endoscopy robotic systems, categorizing their levels of autonomy and intended applications. Of the 28 systems reviewed, most operate at autonomy levels 0 (no autonomy) and level 1 (robotic assistance), with some progress toward level 2 (task automation) in colonoscopy. For more complex procedures, efforts are made to establish master-slave systems. Advances in flexible endoscopic robots are aimed at increasing autonomy, particularly for autonomous navigation within hollow organs. The integration of advanced sensor technologies is critical to achieving precise control and steering in future developments, and thus higher levels of autonomy for these systems.

To employ a brain-computer interface to control an assistance robot could help disabled people to achieve a minimum degree of autonomy and reduce their dependence on caregivers for simple tasks. In this project, we use a textile, capacitive brain computer interface (BCI) to control an assistant robot. Our BCI is based on Steady State Visual Evoked Potentials (SSVEP), which are a response of the brain to a periodic visual stimulus at a fixed frequency. Since the brain signals are in the microvolt range, the noise reduction of external disturbances has a high relevance. Here, we demonstrate a correlation method for detecting and eliminating phase shifts between different measurements for averaging. This method allows measurements in unshielded environment of two microvolts (uV) with an averaging factor of 100 in our non-invasive BCI set-up.

Microwave ablation therapy is frequently used to treat liver malignancies. To ensure proper tumor treatment, intraoperative feedback regarding ablation performance and lesion size is required. By employing an electrode array around the ablation needle, changes in electrical impedance of ex vivo liver are measured in real time. Time-series trends of magnitude and phase are measured for 90 °C and 110 °C ablation temperatures. A finite element model is additionally configured to simulate the underlying biological processes. Gradients in the magnitude and phase trends can indicate the growth of the ablation zone. In combination with a preoperative simulation, impedance-based ablation monitoring can be a possible tool to improve future treatments.

Due to the direct long-term contact with living tissue, the biocompatibility of implants is particularly demanding. In this context, biointegration of implants highly depends on cell colonialization on the surface or in the gap between implant and tissue, as well as cell ingrowth in porous implant structures. Fibrous material is predestined based on two variable parameters, fiber thickness and pore size. Various applications in implant coating, stenting, wound treatment, bone surgery, or cardiovascular field are possible. Coating with cell-friendly material can significantly improve the biocompatibility of the underlying material. In this study, the biocompatibility of medical device samples made of microwire and nanofiber substrates was compared according to ISO 10993-5. In particular, a metal tissue of microwire (stainless steel, 316L) and polymeric nanofiber nonwoven poly-L-lactic acid (PLLA) as well as titanium (Ti) sputtered variants thereof (316L-Ti, PLLA-Ti) were tested in vitro. In order to evaluate their environmental compatibility and their effects on L929 cell line (murine fibroblasts) evaluation was performed via direct contact with 30,000 cells per 48-well in non-adherent plates for 48 h and 96 h. The metabolic activity of cells was determined via CellQuanti-Blue assay, morphological analysis were done using fluorescence staining phalloidin and DAPI. Briefly outlined, microwires were colonized immediately, whereas initially spherical cells settled on nanofiber substrates and later coalesced forming a common cell cluster. The results show independency of structure, ultrathin titanium coating improves cell viability. The findings indicate that the idea of making a surface appealing to cells has potential. It can therefore be assumed that physical vapor deposition (PVD) sputtering of Ti on implant surfaces is a suitable approach to maintain or improve their biocompatibility, regardless of the surface structure of the implants.

Oxygenators are a lifesaving technology used for blood oxygenation and decarboxylation in case of acute respiratory failure, chronic lung disease, and during open-heart surgery. Devices typically consist of a bundle of thousands of fibre membranes in a housing, with gas flowing inside the fibres and blood flowing in the opposite direction outside the fibres. Both ends of the fibre-membranes are fixed with an adhesive to separate gas and blood. The shape of the volume through which the blood flows is determined by the housing of the oxygenator and the internal end surfaces of the fixed ends of the fibre-membrane bundle. The traditional potting process results in a volume shape that is associated with stagnation zones, which are known to promote thrombus formation. In this study, an adapted potting process is proposed which results in a blood compartment with bevelled end faces of the glued bundle parts. Using a numerical study, we have demonstrated that the novel oxygenator design results in optimized flow conditions.

This paper introduces the Transformative Philosophy (T:Phil) program, an initiative designed to embed ethical and socio-political literacy into the technological development process. T:Phil, developed within the Digitalized Lifeworld research cluster and in partnership with leading international technology reflection experts, provides a comprehensive framework for integrating philosophical insights with technical expertise. Aimed at decision-makers in the tech industry, T:Phil delivers tailored content through modular series such as Re:Thinking Technology, which challenge participants to reconsider their approaches to AI, digital infrastructures, and our language of technology. The paper highlights two key modules of the T:Phil program: The first, AI in an Atlas-View, promotes a holistic understanding of AI by examining its technical, human, and environmental aspects. This module encourages participants to view AI as a complex socio-technical ensemble rather than merely a technical innovation. The second module, AI as a Metaphor, explores the metaphorical language used to describe AI, fostering critical awareness of how these metaphors shape our perceptions and influence the ethical considerations of AI deployment. Through innovative delivery methods, including hybrid keynote- discussion events, short series, and intensive Thinkathons, T:Phil engages technical professionals in reflective practices that enhance their understanding of technology’s - especially AI’s - broader implications. The program aims to help cultivate a new cadre of ethically and philosophically informed tech experts capable of designing AI systems that promote equitable and just outcomes. This paper briefly introduces the T:Phil program and provides examples of its application, demonstrating its potential to transform the way we integrate ethical reflection into technology development.

Psychological trials, such as behavioural avoidance tests (BAT), are a fundamental part of the phobia therapeutic process. In order to link physiological reactions with specific points in time during psychological trials, it is necessary to integrate observation data with data collected automatically by sensors, such as wearable devices. To this end, this paper introduces FRAME - a framework for combining real-world events occurring during psychological trials with physiological data collected by wearables. FRAME consists of three parts, an Observation App, a data integration module and a Virtual Reality (VR) App. The Observation App captures events and their exact time of occurrence. The integration module links the observations with the respective physiological data, allowing an in-depth analysis of physiological reactions. The VR App provides a virtual scenario based on the BAT in vivo, thus enabling a BAT in virtuo. The practical applicability of FRAME is tested within a study comparing behavioural avoidance tests in vivo and in virtuo, assessing 25 patients with arachnophobia wearing an Empatica E4.

Laser-induced periodic surface structures (LIPSS) offer the potential to engineer cell adhesion, cell orientation, and cell density on implant surfaces through periodic structures on the nanometer and micrometer scale. Due to their straightforward fabrication on metal surfaces, they provide an essential tool for optimizing implants according to their intended use. However, these types of structures can only be produced on a limited amount of polymers. Within this study, methods of implementing LIPSS on polymer films are investigated using manufacturing processes established in biomedical engineering. For this purpose, a polymer possessing USP class VI certification and ISO 10993 proven biocompatibility is used. The presented processes are easy to implement and can contribute significantly to the implementation of surface active structures like LIPSS on medical devices and commercial implants.

Rehabilitation exercises are a crucial component in the treatment of ankle injuries. With the increasing demand on physiotherapists, many advances have been made in robotassisted ankle rehabilitation, which require an attachment mechanism of the foot or leg on the device. However, securely holding the ankle may cause physical discomfort and for the patient to feel constrained to the mechanism. This paper introduces a gripper of the plantar surface of the foot for ankle rehabilitation employing a universal Granular Jamming Gripper (GJG). It consists of a granule filled membrane that allows the patient’s foot to be immersed in it when it is filled with air and hardens around the foot when vacuum is applied. The paper also investigates the optimal filling percentage of the membrane, membrane thickness and the effect of granule types of different physical properties (ground coffee, table salt, coffee/salt mixture, and granular sugar) on the gripping ability and overall function of the GJG. Tensile and immersion tests demonstrated the suitability of the GJG for gripping a foot model as well as the increased gripping function with a 0.25 mm membrane filled to 60 % with granulated sugar.

A modern area of research in cancer treatment is magnetic drug targeting (MDT) with superparamagnetic iron oxide nanoparticles (SPIONs). In order to understand the processes involved in MDT in more detail and to be able to perform this therapy as efficiently as possible, a monitoring system for the spatial distribution of SPIONs in biological tissue is required. One approach is to use magnetomotive ultrasound (MMUS) to monitor the spatial distribution over time. However, the spatial distribution of SPIONs cannot be quantitatively determined applying basic MMUS algorithms. Therefore, MMUS has been extended by a simulation part to quantitatively determine the spatial distribution of SPIONs. This extended MMUS algorithm requires the material parameters and the geometry of the target tumorous tissue. In this contribution, we describe an ultrasound-based eigenfrequency analysis combined with an iterative inverse simulation-based method to determine the mechanical parameter Young's modulus of tissue mimicking phantoms. The presented approach yields a good estimate of the Young's modulus compared to the result from a compression test.

Additive manufacturing with Arburg Plastic Freeforming (APF), an extrusion-based additive manufacturing process, was used to produce biomimetic bone scaffolds for assistance of osteosynthesis made of poly-caprolactone (PCL) with channel structures for the ingrowth of blood vessels. The specific creation of defined channel structures is very important to support the ingrowth of blood vessels, which significantly supports the regeneration of natural tissue. The tibia model used was divided into the corticalis and the spongiosa to be able to compare the realization of the different requirements of the two natural structures with the printed structures separately. Microtome cuts of the manufactured bone scaffolds with channels were prepared for characterization with light microscopy. Compression tests were carried out on a tensile testing machine. It was found that the channels in the tibial cortex led to a slightly higher stiffness compared to those without channels due to the frame outlines. Overall, however, the stiffness of the Polycaprolactone (PCL) scaffolds was significantly lower than that of natural bone. In the spongiosa scaffolds, the stiffness with channels was lower than that without channels, but still within the physiological range, as was the model without channels. The porosity was within the required range for both scaffold types.

Non-invasive, real-time venous oxygen saturation (SvO2) measurements provide the potential to improve health outcomes in transfusions, ventilator care, and in the intensive care unit. Current methods use catheters which are invasive, expensive, and pose potential high-risk, with only discrete measurements from a jugular vein obtained. This research designed, developed, and tested a proof-of-concept optical sensor similar to a pulse oximeter for non-invasive, continuous SvO 2 monitoring at the external jugular vein (EJV). Testing on three subjects met the aim of correctly identifying the EJV waveform and provided an SvO 2 estimation within the usual healthy range of 60-90%. SvO 2 estimates from the EJV pulse of 71.1%, 72.2%, and 70.4% and breathing pulse of 74.7%, 75.3%, and 74.1%, all fall within range, with trials capturing clear EJV waveforms from the single-point sensor. Further research is necessary to calibrate and validate the device against gold-standard blood gas analysers. However, the initial results prove the sensor’s reliability and potential in detecting and estimating SvO 2 non-invasively, which has the potential to benefit patients across a wide range of clinical settings.

Neuromuscular fatigue can be monitored using complexity domain analysis of surface electromyography (sEMG) signals. This study provides a technique to detect fatigue induced change in signal complexity using the entropic measurements of its recurrence pattern. For this purpose, fifteen participants have been recruited to perform isometric fatiguing exercise and signals are acquired from the biceps brachii muscle of the dominant hands. Thresholded recurrence plot is constructed from the non-fatigue and fatigue segments of the signal. Bidimensional dispersion entropy (DispEn2D) is calculated to quantify the textural change in the resulting recurrence plot. The results show that the present approach can distinguish between non-fatigue and fatigue states. Decrease in the DispEn2D values in the fatigue state shows there is an increase in periodic component of sEMG signal during fatiguing contraction.

In this paper, a thorough analysis of nanosecond laser texturing on Ti-6A1-4V ELI material was carried out, with a particular emphasis on the modulation of laser process parameters and their effects on surface morphology, elemental composition, microscale topographical features, and wettability. The laser-textured samples exhibit unique surface morphologies that were identified by different topographical metrics such as average surface roughness (Sa), surface development ratio (Sdr), peak-to-peak distance (dp), and peak-to-valley height (hp) along parallel and perpendicular to laser scanning directions. The energy dispersive x-ray spectroscopy (EDS) was used to examine the differences in elemental composition and crystallographic constitution of the samples revealed using the x-ray diffraction analysis (XRD). Further contact angle measurement in both the parallel and perpendicular directions revealed the hydrophobicity of the laser-textured surfaces. Out of 4 samples, LT-2 and LT-4 samples displayed noticeably larger contact angles, demonstrating a high degree of hydrophobicity in both directions. Improved hydrophobicity of the textured surfaces may lead to reduced bacterial attachment, whereas anisotropic surface features at the microscale may provide contact guidance to osteoblast cells, opening up avenues for regenerative tissue engineering on biomaterial interfaces.

X-ray diffraction (XRD) is commonly used to analyze phase compositions of crystalline samples. Medical applications include the analysis of biotechnological materials and gall- and kidney stones, where composition can inform pathology assessment. XRD analysis methods like Rietveld refinement requires expert knowledge, and multi-phase sample analysis is especially challenging and time consuming. Largescale medical and biotechnological experiments can therefore be hindered by the need to perform analysis tasks using XRD. Here, we present preliminary results on an automated convolutional neural network (CNN) based method for sample composition analysis using XRD patterns. It can aid experts’ analysis using initial estimations, and enable basic judgements for non-experts. Furthermore, we confirm the intuitive notion that analysis performance degrades with sample complexity through systematic investigation using a synthetic dataset.

Medical imaging is an essential aspect of modern medicine, providing a non-invasive technique for the diagnosis, monitoring and treatment of a wide range of conditions. However, there is a constant need for advancements to reduce radiation exposure, enhance patient comfort, and increase accessibility. As a cheap, contactless and non-hazardous imaging method, Magnetic Induction Tomography (MIT) could offer a new alternative to established imaging methods. First introduced in the early 1990s, MIT is still at the basic research stage and under constant change in the technical requirements. The focus of this study is a recently developed planar setting that offers several advantages, particularly in the image reconstruction of voluminous bodies in a biomedical setting. Due to the novelty of this structure, it is necessary to fundamentally reexamine both the forward and inverse problem of MIT. Here, a deep neural network is used to compare an established receiver setting with a new receiver design to improve reconstruction quality. Additionally, a testing method for new receiver setups is introduced.

Traumatic Brachial Plexus Injury (TBPI) causes arm paralysis, impacting daily activities and reintegration into work. This paper presents the results of the first round of a quantitative Delphi study. The aim is to gather insights from five expert groups. The investigated user requirements primarily encompass design aspects like weight, fitting, and adjustability to accommodate adaptations. Besides increased functionality, a benefit for psychological recovery is presumed.

For people with physical disabilities, it is often desirable to regain control over their personal environment and communication tools. This paper introduces a novel Human- Machine Interface (HMI) using one-shot learning for individualized control signals without extensive training or specialized hardware. Our work suggests a modular system that utilizes common, easily accessible devices like webcams to interpret user-defined gestures and commands through a single demonstration. As a feasibility study on healthy volunteers, we investigate the control of a computer mouse by head movements only. We demonstrate the technical details of the HMI and discuss its potential applications in enhancing the autonomy and interaction capabilities of users with disabilities. By combining usercentric design principles with the advancements in one-shot learning, we aim to forge a more inclusive, accessible path forward in the development of assistive technologies.

Magnetic nanoparticles (MNPs) can be used in various biomedical applications, such as magnetic drug targeting (MDT) or magnetic hyperthermia for cancer treatment. MNPs are injected into the body and accumulated with an external magnetic field at the tumor site. For therapy monitoring, the MNPs distribution has to be accurately mapped in real time. Magnetomotive ultrasound (MMUS) is a suitable technique for this purpose. It was already demonstrated that MMUS can map the accumulation process of MNPs during MDT. Moreover, inverse MMUS is an advanced technique to quantify MNP distribution. This method simulates the displacement of MNPs and surrounding tissues and compares the results to measured data. Understanding how magnetomotive displacement behaves in various elastic environments is crucial for advancing inverse MMUS and accurately quantifying MNP distributions. In this study, ultrasound tissue phantoms were fabricated with varying elastic parameters, incorporating MNPs. Subsequently, their MMUS displacement is assessed.

Drug-loaded magnetic nanoparticles (MNPs) accumulated by an external magnetic field already showed their efficacy in localized tumor treatment in animal studies. Furthermore, specific MNPs are sonosensitive, enabling them to generate inertial cavitation when exposed to a focused ultrasound field. This can be used for controlling drug release, as well as for particle detection and mapping. In addition to inertial cavitation, stable cavitation is also a part of the cavitation process, which has not yet been evaluated on MNPs. While stable cavitation is less harmful to surrounding tissue compared to inertial cavitation, it also contributes to enhanced tumor perfusion. Hence, an optimal strategy could involve inducing inertial cavitation only within the tumor region for drug release and mapping, while employing stable cavitation outside the tumor to enhance perfusion. This study aims to investigate stable cavitation mediated by MNPs for the first time, while examining their overall cavitation behavior.

The non-invasive detection of vital parameters is gaining significance in the medical field through the use of various established and novel camera technologies and the use of artificial intelligence for data processing. A recently emerging type of imaging sensor, the neuromorphic camera, mimics the way the human eye perceives changes in its vision by responding to local changes in brightness for each pixel. The use of these neuromorphic cameras in the medical field is mostly unexplored and offers new possibilities. This research experiment is designed as a proof of concept for the use of neuromorphic cameras to detect the heart rate and respiration rate of a person by monitoring movement and subtle vibrations of the abdominal and chest area. The experimental setup consisted of a DVXplorer camera positioned 30 cm from a healthy person, directed at the abdominal and chest area. The respiration rate was detected with a mean absolute error of 0.66 bpm. The heart rate was detected with an accuracy of 95 % and a mean absolute error of less than 2 bpm. This shows the possibility to detect the heart rate and respiration rate of a person reliably, using neuromorphic cameras under controlled conditions. Future research will focus on improving reliability under more challenging environmental circumstances like varying light conditions and movement of the patient.

Systems based on smart materials have become an increasing research focus in recent years, as their properties provide opportunity for higher functional integration, improved energy efficiency and completely new functionalities. Magnetic shape memory alloys (MSM) provide a promising option here. However, they have received limited attention in the field of medical engineering. This paper therefore aims to provide an introduction into the (magneto-mechanical) properties of MSM and to give an overview of existing applicationsoriented research, both in general and in medical engineering. In the reviewed literature, NiMnGa has been used in several prototypes of micro pumps for intracranial drug delivery and lab-on-a-chip systems. FePd, another MSM alloy, has aroused interest in research into temporary implants.

Pre-eclampsia is one of the most common causes of maternal and perinatal death worldwide. It is a hypertensive pregnancy disease with reduced perfusion of numerous organs. The main symptoms of the disease include elevated blood pressure, proteinuria and the associated subcutaneous fluid retention in the fingers, hands and face of the pregnant woman. In this paper a concept of a portable prototype for early-detection of preeclampsia is presented. The assembled sensor systems enable portable monitoring of the main symptoms to detect changes at an early stage. The sensor system for determining blood pressure is based on photoplethysmography. The sensor system for detecting subcutaneous fluid retention uses the absorption bands of the fluid when illuminated with near-infrared light. For the validation experiments, finger models made of silicone with different fluid retention levels were produced. The results show a correlation between the measured transmitted light intensity and the fluid retention. The results represent a possibility for the portable measurement of important vital parameters in connection with preeclampsia.

In recent years, the popularity of veganism has surged, prompting a growing interest in its health implications. While vegan diets offer numerous benefits, they also pose challenges due to potential deficiencies in essential nutrients and the presence of anti-nutrients in plant-based foods. This paper addresses the need for a comprehensive understanding of nutrient interactions, particularly those involving antinutrients, by creating a Knowledge Graph. Utilizing Natural Language Processing techniques, essential nutritional entities were extracted from unstructured texts, and a custom Named Entity Recognition model was implemented. These data were then used to construct a Knowledge Graph using Neo4j, a leading graph database. Despite the potential of the developed Knowledge Graph to visualize nutrient interactions, limitations persist due to the scarcity of available data on antinutrient interactions. Nonetheless, the Knowledge Graph provides a promising avenue for exploring and understanding the complexities of nutrient interactions, facilitating future research and informing dietary choices.

Cardiovascular disease is a global health burden. To develop novel treatment options complex in vitro model systems are needed that resemble the pathophysiological situation ex vivo. Nevertheless, current pre-clinical in vitro models for pharmacological research are limited in complexity. Basic cell culture models lack cardiac tissue architecture and intercellular communication, limiting their translational capability. Force measurement methods on ex vivo cultured living myocardial slices (LMS) have been described for contraction analysis studies after compound treatment. Here, we combined LMS with a microphysiological system (MPS) to develop MPSlms as heart-on-chip approach that enables advanced nutrition circulation and integrates electrical pacing (MPSpacer) of the ex vivo cardiac tissue. To optimize the LMS technique, we designed a novel isometric tissue holder (ITH) and miniaturized the LMS format, allowing for extended condition testing and thus refinement of animal experiments. The contractile performance of cardiomyocytes was quantified by applying optical mapping of movement detection, which revealed precise and local variations in contraction within one LMS.

Continuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient privacy by capturing detailed visual information. LiDAR technology presents a non-intrusive alternative for continuous monitoring, offering effectiveness across various lighting conditions due to its use of laser light. It provides high-resolution depth information encoded as intensity, making it an ideal solution for maintaining patient privacy while delivering precise monitoring capabilities. LiDAR was selected as a reference method because of its precision and ability to capture detailed depth information, which are crucial for accurate monitoring. In this project, a solution that leverages LiDAR technology to enhance patient monitoring systems was introduced. By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. The high accuracy of the LiDAR sensor enables precise human tracking, which is critical for timely interventions and ensuring patient safety. This approach accelerates response times, preserves privacy, ensures safety during disease outbreaks, addresses healthcare worker shortages, and facilitates efficient monitoring of multiple patients simultaneously without invasive sensors. The ultimate goal of this project is to integrate this solution into Ambient Assisted Living (AAL) systems for elderly individuals, providing them with a safer and more autonomous living environment. Our trained YOLOv5 model has demonstrated exceptional performance, achieving a Mean Average Precision (mAP) of 99.4% at an Intersection over Union (IoU) threshold of 0.5, with a Precision of 99.6% and a Recall of 99.4%.

In this study, a measurement setup was developed to determine the frictional properties between tissue-mimicking and implant materials. Nitinol and Polytetrafluoroethylene (PTFE) were selected as implant materials, while Polyvinyl alcohol (PVA) hydrogel and silicone were chosen as tissue-mimicking substrates and compared to porcine aortic tissue. The results showed a considerably lower friction coefficient between nitinol and all substrates compared to PTFE. PVA hydrogel showed a lower friction coefficient than silicone in contact to all implant materials, but still a higher coefficient compared to aortic tissue. Moreover, parameters such as PVA concentration and aging through storage influenced the frictional properties of PVA hydrogel. Finally, feasibility of the novel measurement setup to measure friction under physiological conditions as well as a comparison with previous studies are discussed.

Electrical Impedance Tomography is a noninvasive imaging technique that employs electrical currents to reveal the internal conductivity distribution within the body. The study follows a new Radio Frequency (RF) approach, by using a Vector Network Analyzer to provide a multifrequency measurement between 10 kHz and 1 GHz. A plastic and a metallic object were positioned within a saline-filled phantom to test the EIT reconstructed images at different frequencies. The results show that based on the image reconstruction, the material’s conductivity can be distinguished at specific frequencies. However, beyond 10 MHz, the reconstructed images become less reliable until reaching 1 GHz, where the reconstructed image deteriorates significantly. While Electrical Impedance Tomography (EIT) has potential in new applications, its efficacy is limited by the ill-posed problem of the reconstruction. Further development and refinement of techniques, particularly in addressing issues that occur at higher frequencies, are required to realize EIT’s full potential in various fields.

Transcatheter aortic valve implantation (TAVI) for aortic valve stenosis can be seen as a disruptive technology that has become the standard treatment for previously inoperable patients over the past 15 years. A key component of the development process for new transcatheter aortic valve replacements (TAVR) is the hydrodynamic testing as part of real-time functional testing. Although, fundamental requirements, necessary testing methods, as well as functional environments, physiological and pathophysiological stress situations, are defined, resulting testing parameter ranges are only estimated. For this reason, two clinically established TAVR were tested in different parameter settings representing different characteristic load situations on a patient. In fact, the two TAVR with different dilatation concepts were tested under different cardiac outputs (CO) and the resulting effective orifice area (EOA), the closing and the leakage volume was measured and evaluated according to ISO 5840-3:2021. Regarding the two TAVR designs, it can be shown that the high radial force of the Lotus Valve contributes to an improvement in the sealing effectiveness of the TAVR. This is because the annulus can be pushed outward by the stent resulting in a better fit between the TAVR and the vessel wall. Additionally, the lamellar skirt of the Lotus Valve also appears to contribute to improved sealing. In the presented experiments, the smooth abluminal- mounted pericardial skirt of the Evolut PRO bioprosthesis does not seem to contribute to additional sealing. The developed test procedure aims to contribute to the establishment of new in vitro standards. Future work must involve applying the testing methods to additional clinically established TAVR as well as new developments.

Assessment of driver stress, crucial for road safety, can greatly benefit from the analysis of multimodal physiological signals. However, fusing such heterogeneous data poses significant challenges, particularly in intermediate fusion where noise can also be fused. In this study, we address this challenge by exploring a 1D convolutional neural network (CNN) with self-attention mechanisms on multimodal data. Electrocardiogram (ECG) signals (256 Hz) and respiration (RESP) signals (128 Hz) were obtained from ten subjects using textile electrodes while driving in different scenarios, namely normal driving and phone usage (calling). The obtained multimodal data is preprocessed and then applied to a self-attention mechanism (SAM) CNN (SAMcNN) to identify driver stress. Experiments are validated using Leave-one-outsubject cross validation. The proposed approach is capable of classifying driver stress. It is observed that shorter segments yield an accuracy of 64.16% compared to longer segment lengths. Thus, exploring self-attention mechanisms for multimodal signals using wearable shirts facilitates non-intrusive monitoring in real-world driving scenarios.

For treating idiopathic sudden sensorineural hearing loss, prednisolone is commonly used. However, systemic or middle ear injections often lead to insufficient drug delivery to the inner ear, causing ineffective treatment and systemic side effects. An implant inserted into the middle ear and delivering the drug directly to the inner ear offers a promising solution, providing controlled, long-term drug release with potentially better efficacy and fewer side effects. Individualized implants made of prednisolone-containing silicone can optimize inner ear treatment by fitting the patient's middle ear anatomy. To gauge the properties of prednisolone-21- hydrogen succinate containing silicone, samples with different geometries and drug concentrations have been 3D-printed. The shore hardness of samples with three different drug concentrations was assessed. Three different shapes with four different drug concentrations were incubated in artificial perilymph for up to 56 days to evaluate the release rates. The resulting eluates were analyzed via Ultra high precision liquid chromatography coupled with a time-of-flight micro-mass spectrometer. Samples were softer when a higher drug concentration was used. A high burst release of prednisolone after one hour was measured. Afterward, the release rates decreased and reached a relatively constant rate after ten days and stayed there for at least another 46 days. The release rates were multiple times higher when the samples had a higher surface-to-volume ratio. The softer the sample, the higher the release rate, unproportional to the concentration increase.

Determining the position and orientation of a medical instrument is essential for accurate procedures in endoscopy, surgery, and vascular interventions. Recently, a novel sensor based on torsional pendulum-like magneto-mechanical motion has been proposed. This sensor is passive, wireless and inductively coupled to a transmit-receive coil array. This setup allows the determination of all 6 degrees of freedom using the characteristic resonance of the sensor. Additional physical quantities such as temperature and pressure can be measured based on the frequency of the sensor, which mainly depends on the distance between the two involved permanent magnets. In this study, we analyze a sensor composed of two magnetic cylinders with variable magnet-to-magnet distance and a basic physical model based on a dipole assumption. Experimental analysis of the resonance frequency and comparison with the model values show both qualitative and quantitative agreement with an average relative error of only 0.8 %. This validates the implemented model and shows the suitability of our magneticmechanical resonator made from cylindrical permanent magnets for sensing applications.

Recent studies show the feasibility of using local remote photoplethysmography (rPPG) for non-contact blood perfusion assessment by creating spatial pulsatile blood perfusion maps. While global rPPG has been widely studied for its robustness, e.g. for non-contact measurement of heart rate, local analyses pose greater challenges in terms of noise suppression and thus reliability. In this paper, the effect of temperature and illumination changes on signal-to-noise ratio (SNR) perfusion maps is analysed. The results show the importance of consistent temperature and controlled illumination for improving SNR and ensuring reliable blood perfusion measurements using rPPG. This emphasises the need for standardised external conditions for accurate interpretation of the results and medical applicability.

Artificial intelligence (AI) has the potential to support time-critical stroke treatment. In a previous study we demonstrated the feasibility of deep learning based automatic classification for thrombus detection during thrombectomies, a catheter-guided procedure to remove occlusions of cerebral vessels. However, this method has yet to be tested during a live intervention. In this work, we present a setup to integrate AI based support in an angiography suite. A classification PC was connected to the angiography by means of a real-time video connection as well as a research interface for control signals. We found that video conversion in real-time does not affect the classification result in comparison to offline classification of DICOM data. Analyzing 50 video streams of previous cases, the system could classify digital-subtraction angiography (DSA) sequences within 13.3 seconds on average. This processing time can further be reduced to an average of 7.9 seconds with GPU acceleration. Additionally, the system successfully classified two DSA sequences acquired during live thrombectomy, identifying the presence of thrombi in less than 5 seconds. So far, the classification result has only been displayed in the control room of the angiography suite to demonstrate feasibility. In the outlook, however, we also discuss how the result can be displayed directly on the angiography screen.

Cast polyurethane films made of an aliphatic polycarbonate-based thermoplastic polyurethane (Carbothane PC) and poly(ether urethane) (Pellethane) were manufactured and their biostability under a static strain rate was investigated in vitro in comparison to unloaded samples. The PU films were stretched to an elongation of 300% and exposed to hydrogen peroxide/cobalt chloride (H2O2/CoCl2) solution for specific periods of time (up to 63 days). Unstrained PU samples were treated in the same way for comparison. The samples were observed for surface degradation via scanning electron microscopy and the bulk erosion by measuring the weight difference. The surface roughness greatly increased in strained Pellethane with scanning electron microscopy (SEM) evidence of deep cracks and holes or ragged and stretched fractures perpendicular to the direction of stress. Carbothane showed a lower degradation compared with Pellethane under mechanical constraint.

Recent advancements in the field of acoustic stimulation have shown promising effects on the autonomic nervous system, influencing parameters such as heart rate [1] and cognitive performance [2]. In this study, we analyzed polygraphy data from patients with heart failure related central sleep apnea. We compared the Apnea-Hypopnea Index (AHI) during a baseline night with that during a night where acoustic stimulation was applied for 40 minutes before bedtime. Seven out of eight patients showed a significant reduction in AHI during the intervention night (p < 0.01). These preliminary findings suggest that further investigation in a more controlled environment is warranted.

Unilateral diaphragmatic paralysis in children with univentricular circulation poses significant challenges, including impaired hemodynamics and increased postoperative complications. Restoring diaphragmatic function is crucial to mitigate these risks. This study investigates the feasibility of automated stimulation of the paretic diaphragm half using electromyography (EMG) signals from the non-paretic side following transection of the right phrenic nerve. Our findings demonstrate successful automated stimulation, suggesting the potential development of an EMG-triggered synchronized unilateral diaphragmatic pacemaker. This innovative approach holds promise as a novel therapeutic intervention for managing unilateral diaphragmatic paralysis in pediatric patients with univentricular circulation.

As a first step towards a new method for noninvasive hip-stem implant monitoring, this work aims to detect bone defects from time- and frequency-difference electrical impedance tomography. As a proof-of concept, an in-silico model of a thigh with bone and implant is used, and convolutional neural networks are applied to predict size and position of areas where conductivity of the bone is increased. From both, time- and frequency-difference voltages, defect size and position can be predicted with high accuracy if signal-to-noise ratios are sufficiently high. This shows the potential for external impedance measurements using electrodes on a patient’s thigh to detect bone loss in the femur.

Human activity recognition (HAR) is an expanding area of research. Although sensors are becoming more readily available, there is a trend toward minimizing the number of associated sensors for better applicability. Neural networks are often employed for HAR, as they can identify movement patterns within data. To optimize the amount of useful information provided to the network, feature extraction methods are commonly applied. However, these feature methods complicate the data processing pipeline, and thus are less applicable to continuous real-time applications. In this work, we investigate the impact of using quaternions as input features for an LSTM network on HAR, specifically focusing on level walking and stair climbing activities, while also considering the inference time. We demonstrate that combining quaternions and raw IMU data, i. e., acceleration and angular rates significantly enhances classification accuracy without adversely affecting the inference time. By additionally adding an approximate estimate of the vertical position change to the input data, the classification accuracy is further improved to a value of 96.18%.

In anaesthesia and intensive care medicine, managing the multitude of signal and substance lines connected to patients presents challenges, risking dislocations, incorrect treatment, and increased workload for healthcare professionals. Existing solutions for signal and substance line management have seen limited acceptance, lacking universality and flexibility. Addressing these challanges, the “intelligent Medical Organization Device” (iMOD) was developed as a modular tool. iMOD, evaluated through simulator training, demonstrated positive results in intuitiveness and acceptance. Feedback highlighted areas for improvement, focusing on line insertion and fixation strength. The organization device may be a component in reducing treatment time and costs, increasing patient safety and improving the satisfaction of patients and healthcare professionals.

Fused deposition modelling (FDM) technology allows the use of a wide range of thermally processable biocompatible or biodegradable polymers. Biomedical research is currently focusing on the development of 3D printed stents that can be biodegradable, as well as drugeluting. In this proof-of-concept study, a cylindrical substrate was integrated into a commercial FDM printer to establish a process for stent printing. Stents printed on a planar substrate were compared with those printed on the cylindrical substrate. The typical stepped structure along the stent struts on the planar print bed caused by layered printing is avoided with the cylindrical substrate.

Orthodontic tooth movement is related to application of forces and moments to individual teeth. Experimental testing of orthodontic wires helps clinicians to exert adequately dimensioned therapeutic loads. In this work, an existing experimental setup to analyze the force delivery of orthodontic wires is augmented with an algorithm that automatically finds a neutral position in which the wire transmits as little force and torque as possible; to this end, the drives of the setup are traversed to an optimal position based on measurements. The algorithm is first tested with the help of a simulation-based software-in-the-loop framework and then implemented on the real setup. Experimental results show massive time savings compared to manual alignment.

Transcatheter aortic valve replacement (TAVR) has become the standard therapy for aortic valve stenosis in patients with high surgical risk. Understanding the flow dynamics in TAVR is crucial for its evaluation and optimization. Experimental flow measurement by means of Particle Image Velocimetry (PIV) is increasingly applied alongside numerical analyses. This study introduces a novel test rig concept enabling the determination of velocity fields using Stereo-PIV under steady forward flow conditions through a TAVR during the peak systole matching the ISO 5840-1:2021 requirements. The experimental setup utilized an impeller pump to generate steady forward flow through a silicone aortic root model with implanted TAVR. A Stereo-PIV setup captured velocity fields in both ventricular inflow and aortic outflow regions. Test conditions were based on physiological flow rates determined from pulsatile measurements. Illumination of the added particles in the test fluid was achieved using an Nd:YAG laser. Fluorescent polystyrol particles (size: 50 μm) were used for flow visualization. Results showed characteristic flow patterns: a central jet flow entering the TAVR in the ventricular flow field. A jet flow directed towards the sinus side and a recirculation zone forming on the opposite side of the sinus could be detected in the aortic outflow. The width of the recirculation zone increases with distance from the TAVR. Maximum flow velocities were detected at 0.78 m/s for the ventricular flow field and 0.94 m/s for the aortic flow field. This study provides a comprehensive approach for fluid dynamic analysis of TAVR under steady flow conditions, offering insights into flow mechanics performance crucial for device optimization. Further investigations could enhance the PIV - measurement procedure by increasing both the spatial resolution and the tracer particle density within the acquired images for a comprehensive characterization of TAVR flow dynamics.

Patient-reported outcomes (PROs) are used in clinical trials to provide valuable evidence on the impact of treatment on patients’ symptoms, function and quality of life. High-quality PRO data from trials can inform shared decisionmaking, regulatory and economic analyses and health policy. The heterogeneity between the patient groups and the associated differences in morbidity and quality of life can be taken into account by standardization to the general population. As a result, our study shows that despite the retrospective design with the corresponding level of evidence, mesh repair of a ventral hernia leads to an equivalent result compared to tissue hernia surgery.

Round window (RW) stimulation is an application of active middle ear implants. However, its clinical outcomes show a substantial degree of variability due to the mismatch of the diameter of the actuator and the RW membrane, and the surgically challenging for placing the actuator. To address this problem, we developed a new piezoelectric actuator for RW stimulation. Firstly, we specifically designed the actuator’s mechanical structure for the RW stimulation. Then, we constructed a finite element (FE) model of the piezoelectric actuator coupled with the human ear’s RW membrane. Using this coupled FE model, we studied the influence of the actuator’s supporting stiffness and the number of piezoelectric stack layers on the stimulated stapes velocity. Finally, we fabricated the piezoelectric actuator and tested its output on human cadaver temporal bones. The results show that the developed piezoelectric actuator has the advantages of a wide operating frequency range and controlled preload on the RW membrane.

Electrical impedance spectroscopy is promising for intraoperative cancer diagnosis by differentiating tissue based on its electrical properties, yet faces challenges from material nonlinearities and sensor effects. In this study, 3D finite element simulations were performed to analyze geometric dependencies. Two different tetrapolar ring electrode layouts are placed on virtual piece of normal and tumor tissue with each radius varied using Monte Carlo simulations and the resulting impedances and cell constants were evaluated statistically. Additionally, a novel sensitivity quality metric is introduced, enabling the identification of the geometry more suitable for impedance measurements. The results highlight the superior sensitivity quality of the improved geometry. Future research should integrate the geometric Monte Carlo approach with variations of dielectric properties of tissue and optimizing the quality metric taking electrode polarization effects into considerations.

Ultrafine membranes produced via electrospinning exhibit multidirectional strength and elasticity. In contrast to compact polymer films or coatings, they also have an exceptional surface-to-volume ratio, which makes them attractive carrier structures for many biomedical applications, also with regard to the release of bioactive substances. Within the scope of an orientation study we investigated the mechanical properties of biodegradable nanofibrous carrier materials using tensile specimens made of nanoparticle loaded polydioxanone (PDO), polylactide (PLA), polyglycolide (PGA) and polybutylene succinate (PBS). Mass loss and tensile strength as well as pH changes during incubation at 37°C in an unbuffered isotonic saline solution with 10% ethanol were examined within defined time periods up to 33 days. Initial tests with several antibacterial additives, especially TiO 2 and Ag nanoparticles and octenidine were performed.

Robotic systems have the potential to improve the accuracy and efficiency of percutaneous interventions. However, their widespread adoption is hindered by challenges such as high costs and usability deficiencies. To address these challenges, we designed a novel semi-automated robotic system that combines manual and robotic degrees of freedom for needle guidance. Since the initial presentation of the system, we have implemented hardware and software improvements. The objective of this study is to evaluate the accuracy of the system. To achieve this, experiments were conducted that involved the entire process from CT scanning to lesion targeting with a needle, using a phantom that replicated abdominal lesions of varying sizes. A total of 36 needle insertions were performed, and a control scan was conducted each time to evaluate the needle tip position. The results demonstrate promising accuracy, with a mean Euclidean error of 5.8 ± 1.7 mm. Qualitative analysis showed that lesion targeting was successful across different sizes, with a total success rate of 27 out of 36 attempts. Although our prototype shows promising accuracy, there is still room for improvement compared to existing systems. Future efforts will prioritize enhancing accuracy, exploring usability aspects, and validating the system's performance with medical professionals.

Beating-heart surgery is performed to reduce patient trauma but depending on the intervention requires tissue stabilization through vacuum cardiac tissue stabilizers. Current designs either require open chest sternotomy or display significant residual motion when used minimally invasively. Active motion compensation methods require complex control algorithms and expensive technology. We propose a novel design for a vacuum tissue stabilizer with expandable suction feet enabling a larger stabilized area and increased motion reduction. Its performance is evaluated on a heart phantom using a silicon membrane mimicking cardiac tissue properties. The motion of the membrane is captured using a stereo camera and marker points on the membrane surface. The results are then compared to a state-of-the-art tissue stabilizer. The extended stabilizer is found to stabilize a 110% larger area while achieving similar motion amplitude reduction. Thus, extendable stabilizers seem to be a promising solution for improved cardiac tissue stabilization in less invasive beating-heart surgery.

In vitro alveolar models are an important tool study respiratory physiology, investigate lung diseases, develop and test new therapies, and simulate inhalation exposure to environmental pollutants or therapeutic agents. A defined cell culture medium is essential for maintaining consistent and reliable in vitro models. In this study a defined cell culture medium for advanced alveolar models based on epithelial A549 cell line and endothelial EaHy926 cell line is developed. Cellular survival was proven by quantification of LDH release. It was shown that co-culture of the cells enhances cellular survival compared to the endothelial monoculture. The formation of a homogeneous monolayer on laminin coated membranes was confirmed by actin staining. Further, the expression of cell type specific proteins (endothelial: CD31, VE-cadherin; epithelial: ZO1 and E-cadherin) was shown. Overall, we demonstrated a completely defined cell culture medium that can be used for the setup of an advanced in vitro alveolar model for biomedical application like drug development or breath gas analysis.

The extracellular matrix (ECM) represents the natural environment of the cells and consists of various fibrous and non-fibrous proteins. It can be generated by decellularization of native tissue (dECM) or by isolation from cultured cells in vitro (cdECM). In the present study the immunomodulatory effect of dECM from native adipose tissue and cdECM from adipose derived stem cells (cdECM) on monocytes and ASCs encapsulated in gellan gum-ECM hybrid hydrogels was investigated. The monocyte activation test revealed a higher secretion of IL6 and TNFα in monocytes incubated with dECM compared to cdECM. Encapsulated ASCs in gellan gum-ECM-hybrid hydrogels exhibit different cytokine profiles (IL8, IL6, MCP-1, TNFα) when cultured with dECM or cdECM or gellan gum alone. The demonstrated differences in cellular behavior in the present of the two different types of ECM should be considered when using them as a biomaterial for in vitro as well as in vivo applications.

Artificial intelligence is rapidly transforming healthcare, yet in Europe, there are no standardized regulations for AI-based medical devices under the Medical Device Regulation. This gap necessitates independent compliance verification by medical device manufacturers. This paper addresses the regulatory void by proposing a comprehensive training concept. It equips manufacturers with essential artificial intelligence knowledge and outlines current market introduction challenges. Through pre-tests and training sessions, the concept's efficacy will be assessed and refined. In summary, this work offers insights into AI's transformative potential and regulatory hurdles in healthcare, paving the way for improved standards and practices.

Skin wound healing involves many cell types in a stepwise process of tissue regeneration. Reepithelialization is an essential characteristic of successful healing. In tissue engineering, mimicking the complex process of injury repair in vitro is challenging and requires the development of advanced skin models. In this study, a simple and reproducible method for wounding three-layered skin models on membranes with different pore sizes (0.4, 1, 3 μm) was established. The model allows the investigation of reepithelialization processes in a more complex environment. Hemalaun-eosin (HE) and 3-[4,5-dimethylthiazole-2-yl]-2,5- diphenyltetrazolium bromide (MTT) staining proved sufficient removal of the epidermis directly after wounding. An increasing pore size of the culture membrane delayed the reepithelialization time. Transepithelial electrical resistance (TEER) measurements provided non-invasive monitoring of reepithelialization, showing increasing values 4 days after wounding for the skin models on 0.4 and 1 μm membranes but not for those on 3 μm membranes. Cytokine quantification of interleukin (IL)-6 and IL-8 complemented the TEER results with increasing levels directly after wounding for all skin models. This skin wounding model could be used to simulate different wounding scenarios and test wound matrix materials. Furthermore, it could be adapted by adding immune cells to resemble the in vivo setup more closely.

Chewing not only converts food chunks to digestible proportions, it also conveys audible acoustics resulting in a perception on the type and condition of the food being eaten. As biomedical engineers, we may want to reproduce the same eating experience for those who cannot chew or for those who have allergic reactions to some foods. But to understand this psychoacoustic phenomenon better, it is crucial to understand what produces the sound of specific foods. The purpose of this paper is to present a straightforward methodology to produce audible acoustics from a notoriously loud Finnish delicacy and analyse the sound produced. One hundred samples of leipajuusto and one hundred samples of Gouda cheese for controls were subjected to shear between a bamboo board and a wetted blade. All leipajuusto samples and none of the Gouda cheese samples produced audible squeaks. A 0.1-s delay between blade displacement and sound production was observed. We attribute this delay to the buildup to release. The frequency spectra from pushing and pulling movements were observed to have only negligible differences. This indicated that the internal structure between events did not change. Therefore, the hypothesis that a disruptive event underlies the squeaking process is less plausible.

Minimally invasive transcatheter aortic valve replacement (TAVR) procedure has become the preferred procedure for patients with aortic valve stenosis or insufficiency with high risk for conventional open surgery. The favorable clinical outcomes of high-risk patients led to an expansion of the cohort including intermediate and low-risk patients. A critical aspect of advancing TAVR procedures lies in preoperative planning, integrating patient-specific in-silico deployment simulation and post-deployment fluid mechanics assessments. This study introduces a novel approach to calcified TAVR patient shape modeling, addressing this problems. The model integrates an extended mesh generated by DeepCarve, encompassing the aortic arch, and a novel deep learning-based volumetric shape model of calcifications. The key innovation lies in the utilization of a conditional Convolutional Variational Autoencoder (cCVAE) to generate realistic calcification patterns, demonstrating promising preliminary results in matching actual cohort data. Future investigations should focus on data collection from diverse medical centers to validate and refine the proposed methodology. This study showcases significant progress in generating synthetic TAVR patient geometries, incorporating detailed anatomical structures such as the aortic root, valve, and arch, along with volumetric calcification patterns. These findings represent a crucial step towards enabling real-time preoperative TAVR planning, inclusive of patient- specific in-silico deployment simulation and comprehensive fluid mechanics assessments.

In this study, a passive control strategy of inductive power transfer (IPT) using ferroelectric multilayer ceramic chip capacitors (MLCCs) is presented. The required system parameters, i.e., ferroelectric hysteresis, frequency of the IPT, and voltage range across the MLCCs are reported. The receiver circuit consists only of a parallel resonant circuit, a halfwave rectifier and a load; the passive control of the IPT is achieved exclusively by the nonlinear properties of the ferroelectric MLCCs. The stabilization of the secondary output voltage ULoad at constant load is experimentally evaluated for an inductive coupling factor k between 10 % and 30 % for three nonlinear MLCCs #1, #2 and #3. With our proposed passive control strategy ULoad is maintained at -1.2 % and +0.6 % around a median value of 17.3 V (17.1 - 17.4 V) for k ∈ [20 %, 30 %] using MLCC #1, ±0.9 % around a median value of 11.2 V (11.1 - 11.3 V) for k ∈ [10 %, 18 %] using MLCC #2, and -1.6% and +2.6 % around a median value of 5.03 V (4.95 - 5.16 V) for k ∈ [16 %, 30 %] using MLCC #3. The proposed control principle is particularly advantageous for highly miniaturized microimplants, as it allows IPT control without additional semiconductors, sensors and vulnerable communication channels.

Patient-ventilator asynchronies occur during mechanical ventilation when there is a mismatch between the patient’s needs and the ventilator’s settings. Ineffective Efforts during Expiration (IEE) is such an asynchrony, which if undetected can cause patient stress and prolong the ventilation duration. Current approaches to detect IEEs only target adult patients and are not directly applicable to newborns. This work explores the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models to detect IEEs in neonates. An attention mechanism is employed to additionally offer a visual explanation for the network’s classification. All of the tested attention RNN architectures yield an accuracy of over 90%, with LSTMs performing slightly better than simple RNNs. A user study is conducted to evaluate the usability of the employed explainability method. The results show that the used visualizations are intuitive to understand, however the network’s attention itself can be misleading in certain cases.

With growing interest in laboratory automation and high-throughput systems, the amount of generated experimental data is rapidly increasing while analysis methods still require many manual work hours from experts. This is prevalent in X-ray photoelectron spectroscopy (XPS), where quantification is a complex, time-consuming, and error-prone task. We therefore propose a neural network-based workflow to make this process more approachable. As training data availability ranges from insufficient to non-existent, our workflow creates a synthetic dataset containing XPS signals and corresponding area percentages based on binding energies supplied by the user. As a result, no previous measurements are needed. After training on the synthetic data, the neural network can predict area percentages of the known binding energies with high confidence. This workflow can therefore be adapted for XPS quantification tasks to filter significant data and supervise processes. Moreover, this enables non-experts to analyze spectra and can help experts to reduce focus on important spectra.

Within this work, the influence of recovery temperature on thermal induced shape recovery behaviour of injection molded biodegradable poly-(L-lactide) (PLLA) was investigated. Shape retention, shape recovery and shape recovery rate were determined for high molecular weight (HMW) PLLA (320 kDa) and very high molecular weight (VHMW) PLLA (700 kDa) at recovery temperatures of 50, 60, 70, 80 and 90 °C, respectively, using DMA experiments. It was found that for both polymers, shape recovery reached a plateau of around ~70% at 80 °C, which could not be increased at higher temperatures. However, shape recovery rate did show to be affected by higher temperatures. Moreover, it was found that the polymer with the higher molecular mass exhibited a higher shape recovery rate.

In the world of dental implants, the essential determinant of success lies in the phenomenon of osseointegration, wherein titanium implants exhibit the ability for robust integration with the surrounding bone and tissue. Beyond the inherent qualities of titanium which make it an attractive candidate as an implant material, attention has shifted towards its’ tailored surface modifications. These methods help the implant manufacturers to modify the implant surface for successful osseointegration and higher chances of enduring implantation. This review explores different surface modification methods and their impact on the dental implant success.

Specialised cells in the blood play an important role in controlling the immune responses. Thus, these cells are also valuable for the diagnosis and treatment of diseases such as autoimmune disorders and cancer. Trace-less Affinity Cell Selection technology, also known as Fab-TACS technology, is a new approach to precisely isolate specific immune cells from blood samples. Currently, this technology is used in a device called Fabian, which resembles a cassette with a capacity of 60 ml. For diagnostic purposes, however, the sample size must be reduced to a few microliters. For this purpose, a stand-alone setup for the control of a microfluidic cartridge was developed and a sequence for the implementation of the TACS technology was designed and characterized. As the combination of cell purification and subsequent cell counting in a single device can streamline workflows and save time, we also integrated an analysis platform for cell counting into the setup. Instead of using two separate devices, isolated samples can be counted immediately after isolation without interruption. This platform was initially tested with latex particles that were passed through the detection channel of the microfluidic cartridge. The functionality of the developed program sequence and cell counting was successfully demonstrated in verification experiments.

Falls in the elderly population present substantial health risks, often resulting in morbidity and a decline in quality of life. Conventional fall detection methods, including wearable devices and cameras, are hindered by issues such as variable lighting conditions and privacy considerations. Radarbased fall detection has emerged as a promising alternative, providing an unobtrusive method. This study aims to classify fall detection using Smoothed Pseudo Wigner-Ville distribution (SPWVD) images and ResNet-50 model. For this, an online publicly available radar database comprising 15 subjects is utilized. Radar signals is processed into SPWVD timefrequency representation images for analysis. The SPWVD images is fed into the ResNet-50 model. Experiments are performed and performance is evaluated using 10- fold cross validation. The proposed approach is able to distinguish elderly fall. Using ResNet-50 model, the approach yields a maximum average classification f-measure, area under curve score, precision and, sensitivity of 67.15%, 52.31%, 50.55% and, 99.88%, respectively. Hence, the proposed framework holds promise for accurately and efficiently detecting falls among the elderly population within their private environments.

Introduction: Preoperative electrically auditory evoked potentials (= eAEP, i.e., eABR for brainstem, eAMLR for midbrain, and eALR for cortex) can be performed under local anesthesia, achieving subjective sensation and hearing in patients having a functional auditory nerve. For intraoperative eAEP, we stimulate the auditory nerve with an intra-cochlear test electrode like a cochlear implant (= CI) and stimulate basally when using an extra-cochlear electrode. These measurements aim to confirm preoperative findings (e.g. uncertain preoperative eABR) or intraoperative decisions (e.g. vestibular schwannoma resection). Methods: Preoperative eAEP can be performed with the trans-tympanic stimulation electrode and a neurological EP system for recording the responses. In addition, eAEP under local anesthesia allows for preoperative diagnostics of subjective sensation. For intraoperative eAEP, the intra-cochlear auditory nerve test system (= ANTS) system is preferable showing similar results to the gold standard CI-eABR (i.e., eABR using a CI for stimulation). The eAMLR can be used only preoperatively in case of ambiguous EABR or for extended analysis of the auditory pathway. Results: During preoperative eAEP in local anesthesia, an eABR is essential. eAMLR and eALR are optional. The subjective sensation can be recorded in parallel. A reliable response often avoids eAEP under general anesthesia. eAEP under general anesthesia should be performed as rarely as possible preoperatively to avoid unnecessary intraoperative eAEP, performed usually directly before cochlear implantation. Extra-cochlear stimulation avoids mechanical trauma and possible intra-cochlear infections. However, intra-cochlear stimulation is usually recommended due to larger response amplitudes; in fact, the ANTS electrode provides more stable monitoring. Conclusion: eAEP can be diagnostically useful preoperatively and intraoperatively with the concept presented here. Since not all clinics are familiar with these methods, a learning curve may be necessary.

Introduction: In auditory evoked potentials (= AEP), auditory brainstem response (= ABR) is the gold standard for objective analysis of the auditory pathway using acoustical stimulators like headphones during hearing screening and patient assessment. Where no response can be recorded, electrical stimulation can be used in pre-, intra-, or postoperative scenarios to confirm the excitability of the auditory nerve (= eAEP). In eAEP, the recording of brainstem response (= eABR) is mostly the method of choice. Sometimes in eABR, the stimulation artifact can not only harden but even prevent the detection of the response waveforms. Methods: Many factors can influence the stimulation artifact when recording an eAEP. Neurological recording systems are recommended as well as some approved AEP systems traditionally used in ENT for amplification to avoid large stimulation artifacts in the response waveforms, but able to recover in <1 ms from saturation. High stimulation rates (~90 Hz) can lead to overlapping of the response waveforms with the following stimulation artifact. In preoperative eAEP, stimulation artifacts are often high due to extra-cochlear stimulation. In intra- and postoperative eAEP, the artifact is lower and mostly similar across patients. Results: The recommended recording systems show low stimulation artifacts in almost any case for intra- and postoperative eABR. In contrast, later responses, like electrically evoked auditory middle-latency response (= eAMLR) and electrically evoked auditory late response (= eALR), are less affected by artifacts. Conclusion: The stimulation artifact can be a problematic factor. Depending on the EP system, intra- and postoperative eABR can be challenging when trying to record the response waves. Where possible, clinicians should think about eAMLR or eALR for results with less stimulation artifact. Finally, the most important factor remains using a recording system that can handle the stimulation artifact. We do not recommend recording devices with high artifacts for preoperative eABR. For intra- and postoperative eABR, the recommended systems can prevent challenging recording.

Digitalization in the healthcare sector is progressing rapidly. In the field of measuring instruments for manual dexterity, digitization has resulted in major limitations in terms of ease of use and usability. This study describes the development of a digital nine-hole peg test (dNHPT) for assessing hand dexterity that can be used just as easily and quickly as the original nine-hole peg test. The extensive validation of the dNHPT includes the investigation of validity, reliability, clinical utility and user-friendliness This paper presents the validation of clinical utility and usability of the dNHPT. The comparison of the original ninehole peg test (NHPT) und the dNHPT show similar clinical utility of the dNHPT and the NHPT (74% of all ratings of dNHPT are equal to or better than the NHPT) and good to excellent System Usability Scale Scores of 78 (CI95: 75;81) and 85 (CI95: 80;89). Several recommendations for further development of the dNHPT were identified. The newly developed dNHPT presented here supports the quality of data collection through automated time measurement and result presentation and shows good usability and clinical utility ratings.

Breast cancer (BC) poses a significant worldwide health challenge, necessitating the identification of its molecular origins and the development of possible treatment approaches. In this investigation, we introduce a pioneering methodology integrating genomic data and machine learning algorithms to identify genes associated with BC and their corresponding drugs. Initially, RNA-sequencing data of normal and malignant BC tissues publicly available in the NCBI GEO database were pre-processed using a standard pipeline. Further, machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used to identify the differentially expressed genes (DEGs). The results were validated based on accuracy, sensitivity, specificity, precision, and F-score. Moreover, we identified the drugs corresponding to DEGs using the DepMap database. Our results revealed that genes such as OC90, KLK9, CXCL10, CDRT1, LCN6, GOLGA7B, and ZNF223 were commonly identified by all three machine learning algorithms. We found that the drugs DIHYDROROTENONE, 4-IODO-6-PHENYLPYRIMIDINE, BMS-754807, ARRY- 886, ESTRAMUSTINE-PHOSPHATE, FR-122047, and PIK 93 produced high correlation values on gene-drug interaction. The present study emphasizes the significance of utilizing genetic data and powerful machine learning algorithms to decipher the intricacies of BC biology and expedite the creation of tailored therapeutic approaches.

This paper examines the challenges and opportunities associated with the implementation of Electronic Health Records (EHR) globally and, specifically, in Germany. While EHRs promise improved accessibility and comprehensibility of patient data, healthcare professionals face challenges such as resource constraints and the need for user-friendly interfaces. The focus is on the design of the graphical user interface (GUI) to enhance accessibility for both healthcare professionals and patients. The proposal suggests a three-dimensional digital human model as the basis for organizing and retrieving medical documents, integrating visual and chronological aspects. Additionally, the paper explores the potential benefits of incorporating personal health data, addressing data access separation concerns for users and promoting interdisciplinary information exchange. The concept-study concludes by highlighting the importance of a well-designed user interface in facilitating effective utilization of EHR systems.

For applications in the cardiovascular field, the production of tailored biomaterials such as those required for insulations or patches is a key technology. In this work we were able to manufacture thermoplastic silicone polycarbonate elastomer scaffolds with a porous external surface. The samples were produced using the dip coating process. Pores were created using the salt leaching technique. The complete removal of the salt was verified by Raman microscopy. The absence of solvents was demonstrated by biocompatibility tests using an endothelial cell line. The potential functionality of the system was thus demonstrated. The next step will focus on promoting the adhesion of suitable cells for specific applications.

Extracting structured information from scientific works is challenging as sought parameters or properties are often scattered across lengthy texts. We introduce a novel iterative approach using Large Language Models (LLMs) to automate this process. Our method first condenses scientific literature, preserving essential information in a dense format, then retrieves predefined attributes. As a biomedical application example, our concept is employed to extract experimental parameters for preparing Metal-Organic Frameworks (MOFs) from scientific work to enable complex and information-rich applications in the biotechnology-oriented life sciences. Our open-source method automates extracting information from verbose texts, converting them into structured and easily navigable data. This considerably improves scientific literature research by utilizing the power of LLMs and paves the way for enhanced and faster information extraction from extensive scientific texts.

Titanium and its alloys play a vital role in dental implantology, providing a stable foundation for replacement teeth and restoring oral function effectively. Titanium alloys, particularly CP-Ti and Ti-6Al-4V, are widely utilized due to their biocompatibility, corrosion resistance and mechanical properties. However, concerns arise regarding the release of cytotoxic elements and stress shielding effects. Addressing these concerns requires advancements in material design and tribological properties. Research focuses on developing low modulus β-type Ti-based alloys with improved wear resistance and biocompatibility, incorporating elements like niobium, tantalum or zirconium. This review explores the tribological implications of these advancements, focussing on the implantabutment interface, emphasizing the need for optimized titanium alloys for dental implant applications.

The flow control of inspiratory valves for mechanical ventilation is a crucial element of the overall functionality as it mainly governs precision and safety. However, control tuning can be challenging as the widely used magnetic actuation principle comes with a hysteresis shaped characteristic. As a result, the production of ventilators is highly dependent on specific valves and therefore vulnerable if supply chains are interrupted as during the pandemic. An approach which is capable of providing a reliable control for a variety of standard solenoid valves would be beneficial. Therefore, this work examines different models of hysteresis and their precision when being applied to actual measurement data of high pressure solenoid valves as a foundation for a following model-based control approach which can be either feedback or feedforward. Regarding the precision, the Prandtl-Ishlinskii (PI) model outperformed alternative approaches (MAEbelow2.5 L/ min) for the chosen parameters which is why the model is described for fitting both the forward and inverse case.

Poly(ethylene glycol) diacrylate (PEGDA) is a common polymer in the field of biomedical engineering and can be used for the production of drug delivery systems (DDS). The main advantages of PEGDA are biocompatibility and the ability to alter the physical and chemical properties, thereby ensuring individualized drug release behaviour. The processing of PEGDA via inkjet printing is relevant for the production of DDS. This can be challenging due to the high viscosity of pure PEGDA. In this work, PEGDA, with a molecular weight of 250 g/mol (PEGDA250), was inkjet printed using a Nanoplotter 2.1 with a piezoelectric heatable NanoTip HV-J-H printhead (GeSiM mbH, Radeberg, Germany) at different voltages and temperatures. Droplet generation was analysed in terms of droplet volume and angle deviations. PEGDA250 can be inkjet printed reproducibly in a voltage range of 60 V - 80 V at room temperature (20 °C) or heated up to 38 °C. The average volume of heated (38 °C) PEGDA250 droplets was approximately 110 pl - 150 pl higher than the droplet volume of PEGDA250 in the unheated state. The average angle deviations of main and satellite droplets were mostly < 3°. Increasing voltage or excessive heating of more than 38 °C caused greater instabilities in the droplet generation as well as larger satellite droplets which can affect the accuracy negatively. The studies have shown that PEGDA250 can be processed via inkjet printing and thus can be used as a drug carrier for DDS without the need for mixing with a solvent.

The current German medical S3-guideline on dementia recommends the use of cognitive training and stimulation, e.g. through shared games, for mild cognitive impairment and mild dementia. The use of cobots, which allow direct human-robot collaboration, enables people with paralyzed upper limbs to actively participate in social activities. This research presents a novel approach through the development of a voice-controlled board game specifically tailored to the inclusion needs of people with severe disabilities. The human voice commands recorded via USB microphones are digitally filtered. Speech recognition of the control commands is performed using a Convolutional Neural Network (CNN) based on the VGG- 16 architecture. The robot´s activity is controlled utilizing the ROS 2 robot operating system. A portable table-based complete system with a 3D-printed Tic-Tac-Toe playing field and robot assistant for severely disabled paralyzed people has been developed. The robot control system employs a pick-and-place mechanism, seamlessly integrating with speech recognition to enhance gameplay interaction. The CNN model achieves an impressive accuracy rate of 97%, ensuring reliable speech recognition performance throughout gameplay. The targeted integration of robotic technologies and artificial intelligence opens new avenues in the prevention of mental illness, care support and inclusion of older and disabled people.

Magnetic induction tomography (MIT) is an imaging technique that uses eddy current effects to map the conductivity of low conductive objects. It has a wide range of applications and offers numerous possibilities in various fields. One task of this imaging method involves resolving the ill-posed and nonlinear inverse problem. However, existing methods have weaknesses. A hybrid approach that integrates theoretical algorithms with machine-learning methods is proposed as an innovative solution to the challenges of MIT reconstruction.

Pulsatile fatigue testing is a basic requirement for arterial stents, however, there are several opportunities to setup and conduct the testing. The recently published standard guide ASTM F2477:2023 provides specific guidance, in particular includes the pressure controlled test method applying an outer hydraulic pressure to the test samples. The current work describes this approach, and demonstrates that the technical requirements can be met successfully. In addition, some inherent advantages are discussed, e.g. accuracy of pressure loading, absence of stent migration and potential high test frequencies.

Orthopaedic insoles are necessary for correcting foot deformities and providing customized support. This paper investigates the optimization of insole design through computational methods, with a focus on generative design (GD) techniques. By integrating advanced design methods with biomechanical analysis, this study aims to develop customized insoles that effectively treat foot disorders. To adequately account for all relevant forces acting on the foot during gait, a biomechanical load model is created. In addition to vertical forces, the model includes horizontal forces (anterior-posterior and medial-lateral) that occur parallel to the walking surface. GD can optimize the insole design to distribute pressure and support areas of the foot appropriately by creating functionally graded lattice structures. The study examines the potential of GD in creating insoles and the impact of the load model on the design outcome. The design process includes dynamic pedography and gait analysis to ensure that the insoles are tailored to the patient’s individual needs. Future research challenges include incorporating horizontal forces and minimizing mass while maintaining support. The study highlights the potential of computational methods, such as GD and artificial intelligence, in optimizing the design of orthopaedic insoles to ultimately improve patient comfort and mobility.

The ISO 14801 standard test is repeatedly criticized for not reflecting the actual conditions and loads realistically enough to which dental implants are exposed (e. g. during chewing). The aim of this paper is to provide a brief review of the test parameters of the ISO standard and to compare them critically with the parameters that prevail in vivo. The research revealed significant simplifications of the test conditions, wich can partly be justified with better reproducibility and comparability. Nevertheless, the realization remains that the test deviates considerably from real physiological loading conditions and should be adapted accordingly or supplemented by further tests.

Anatomy-based fitting (ABF) has the potential to lead to better outcomes than conventional clinically-based fitting (CBF). It has already been shown that anatomy-based fitting based on postoperative imaging (postopABF) can improve speech understanding. Moreover, it was demonstrated that patients preferred anatomy-based fitting maps based on preoperative imaging (preop ABF) over CBF. The goal of this study was to examine if a difference between preopABF and postopABF could be observed. Via the software OTOPLAN4 (CAScination, MED-EL) the electrode position was calculated based on preoperatively, as part of the clinical routine, obtained imaging (CT or MRI). If necessary, after the cochlear implantation surgery the selected electrode array was adapted if a different array was chosen intraoperatively. This data was exported into the fitting software MAESTRO to enable a preopABF. The same process was done based on postoperative imaging (CT) so that 1) the frequency bands were compared between preopABF and postopABF data and 2) the difference was displayed in semitones. 27 Patients could be included who got a postoperative CT scan. In addition, subjective tinnitus and dizziness or imbalance issues did not significantly change from the pre-operative appointment over the first fitting to the control fitting one month later. In this study, we could show that there is no significant difference between apical to medial and a significant difference for medial to basal electrode contacts between the preopABF and postopABF regarding the frequency bands. Thus, preopABF is an easy, clinically practicable way to implement ABF, which is promising to lead to better outcomes without extra costs and the radiation burden that comes with postopABF. If patients subjectively prefer either preopABF or postopABF is still to be examined.

In laparoscopic liver surgery, image-guided navigation systems provide crucial support to surgeons by supplying information about tumor and vessel positions. For this purpose, these information from a preoperative CT or MRI scan is overlaid onto the laparoscopic video. One option is performing a registration of preoperative 3D data and 3D reconstructed laparoscopic data. A robust registration is challenging due to factors like limited field of view, liver deformations, and 3D reconstruction errors. Since in reality various influencing factors always intertwine, it is crucial to analyze their combined effects. This paper assesses registration accuracy under various synthetically simulated influences: patch size, spatial displacement, Gaussian deformations, holes, and downsampling. The objective is to provide insights into the required quality of the intraoperative 3D surface patches. LiverMatch serves as the feature descriptor, and registration employs the RANSAC algorithm. The results of this paper show that ensuring a large field of view of at least 15-20% of the liver surface is necessary, allowing tolerance for less accurate depth estimation.

In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining high-quality object masks. We introduce a HoloLens-Object-Labeling (HOLa) Unity and Python application based on the SAM-Track algorithm that offers fully automatic single object annotation for HoloLens 2 while requiring minimal human participation. HOLa does not have to be adjusted to a specific image appearance and could thus alleviate AR research in any application field.We evaluate HOLa for different degrees of image complexity in open liver surgery and in medical phantom experiments. Using HOLa for image annotation can increase the labeling speed by more than 500 times while providing Dice scores between 0.875 and 0.982, which are comparable to human annotators. Our code is publicly available at: https://github.com/mschwimmbeck/HOLa.

Digital biomarkers derived from mobile sensors could enable more frequent and objective monitoring of motor disorder symptoms. In this study, we examined the sensitivity of five tablet-based tests in predicting changes in the MDS-UPDRS-III scores following controlled Levodopa administration, utilizing regression models trained on data from 28 patients. Our experiments revealed that the best-performing single-test models exhibited an inverse prediction in 5 patients. A combined model incorporating two tests reduced misclassification to three patients. Further stratification of patients into symptom-specific groups, based on the presence of medication-induced tremor changes, and training two subgroup-specific models led to only one subject being misclassified. These results underscore the potential of mobile device biomarkers for prediction of symptom changes in Parkinson’s disease patients and the need for individualization.

Vibroacoustic signals generated by the interaction of moving clinical devices, e.g. an aspiration or biopsy needle, with different tissues creates a distinct signal. These signals can be received via a dedicated audio sensor and subsequently analysed with the potential to provide information about location, tissue characterisation, and event classification. With that it could also be used as an additional and complementary guidance tool particularly for future robotic assisted procedures. In our laboratory research we used different phantoms with animal and artificial tissues, audio pre-processing, and subsequent training and optimisation of a ResNet model with a tissue event detection F1 score of 95.3% when compared to video based annotation tool. This result is very encouraging, as several possible improvements have been identified that will be implemented in the next research steps together with a robot assisted insertion and an automatic video annotation algorithm.

Although rehabilitation therapies are still the most common treatment for those who have had a stroke, they can be dull and repetitive. Despite recent advances in the field of rehabilitation robots, they can be complicated to operate and needs plenty room space in most cases. This paper presents a gaming controller (GC) that acts as an interface system between the rehabilitation system and the patient. It is connected to a mechanically actuated ankle rehabilitation mechanism, which incorporates the usage of the patients’ upper limbs to work on the range of motion of the ankle. The mechanical mechanism (MM) provides the base for gamified ankle rehabilitation; adopting the mechanism with the developed GC that comprises commercial games. The GC translates the rotary actions of MM to corresponding controller in the games.

Heart rate variability (HRV) is an important marker in various cardiovascular and non-cardiovascular conditions. This study aimed to evaluate the effectiveness of three linear models (Logistic Regression, Ridge Regression, Support Vector Machine) in distinguishing between healthy individuals and those with valvular heart diseases (VHD) using time domain and frequency domain HRV indices derived from electrocardiographic (ECG) signals. We analyzed 59 recordings taken from two public datasets containing electrocardiographic, seismocardiographic, and gyrocardiographic signals from “Mechanocardiograms with ECG reference” and “An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases” that contain 29 and 30 recordings, respectively. HRV analysis included time and frequency domain indices and the linear models were evaluated using 5-fold stratified cross-validation. The highest sensitivity, PPV, accuracy and F1 score were observed for Logistic Regression (0.8810, 0.8819, 0.8814, 0.8812), followed by Ridge Regression (0.8805, 0.8858, 0.8814, 0.8808), and the lowest were observed for linear SVM (0.8310, 0.8318, 0.8305, 0.8305). The results showed that it is possible to distinguish healthy volunteers and patients with linear classifiers and time domain and frequency domain HRV indices obtained from ECG signals with decent performance.

In vitro test setups for simulated application testing are necessary for physician training as well as for development and benchmarking of various devices in interventional neuroradiology. Within the current study, we implemented a test setup for in vitro simulated application testing. Trackability in the distal catheter area, which was positioned inside a complex 3D vascular model was analyzed for two neurovascular stent systems (Neuroform EZ 4.0x20 mm and Enterprise VRD 4.5x14 mm) combined with different microcatheters (Excelsior XT-27 Flex, PX Slim Delivery Microcatheter and Prowler Select Plus Infusion Catheter). The described test setup allows for a diffentiation of neurovascular stent systems with regard to the trackability force as a major procedure related indicator for device handling. Therefore, the simulated application system presented within the current work represents an indispensable tool for safely and efficiently developing and evaluating new technologies for interventional neuroradiology prior to clinical implementation.

The integration of Artificial Intelligence (AI) into healthcare represents a transformative shift, offering opportunities for enhancing patient care, diagnostic accuracy, process optimization and treatment pathways. This research sets out to forge a strategic management decision support framework for leveraging AI within the healthcare sector, aimed at systematically exploring and integrating AI innovations to bolster the patient health outcomes. By creating a comprehensive categorization system, we attempt to navigate the complex array of possible AI applications within the field of healthcare, hence enabling the identification, selection, and advancement of AIdriven initiatives. Through a blend of systematic literature review and expert insights, this study maps possible AI applications across dimensions like ‘medical disciplines’, ‘healthcare processes’, ‘AI research areas’, and ‘user groups’. By reflecting the diverse perspectives, this system transcends mere classification and stands as a cornerstone for identifying, selecting, and developing AI-driven medical use cases to guide strategic implementations of AI within clinical settings. This multidimensional system offers a blueprint for healthcare entities to strategically navigate the AI landscape, enabling them to make informed decisions about technology adoption and change management processes, ultimately leading to improved patient care and operational efficiency.

The lack of labeled, intraoperative patient data in medical scenarios poses a relevant challenge for machine learning applications. Given the apparent power of machine learning, this study examines how synthetically-generated data can help to reduce the amount of clinical data needed for robust liver surface segmentation in laparoscopic images. Here, we report the results of three experiments, using 525 annotated clinical images from 5 patients alongside 20,000 synthetic photo-realistic images from 10 patient models. The effectiveness of the use of synthetic data is compared to the use of data augmentation, a traditional performance-enhancing technique. For training, a supervised approach employing the U-Net architecture was chosen. The results of these experiments show a progressive increase in accuracy. Our base experiment on clinical data yielded an F 1 score of 0.72. Applying data augmentation to this model increased the F 1 score to 0.76. Our model pre-trained on synthetic data and fine-tuned with augmented data achieved an F 1 score of 0.80, a 4% increase. Additionally, a model evaluation involving k-fold cross validation highlighted the dependency of the result on the test set. These results demonstrate that leveraging synthetic data has the ability of limiting the need for more patient data to increase the segmentation performance.

This study presents a novel approach to augment physiotherapeutic exercise datasets by synthesizing realistic Inertial Measurement Unit (IMU) data. The augmented dataset is used to improve the performance of a deep learning based exercise evaluation system. The approach is demonstrated using the deep squat exercise from the Functional Movement Screening (FMS) protocol. By integrating musculoskeletal simulation and leveraging knowledge of potential movement errors based on FMS evaluation criteria, we aim to produce synthetic data that closely mimics human movement. Our evaluation demonstrates that training a combination of a Convolutional Neural Network with a Long-Short-Term-Network (CNN-LSTM) with both real and synthesized data significantly improves the model’s performance, especially in generalizing to unknown subjects. However, limitations such as the approach’s specificity to the deep squat exercise suggest the need for a more adaptable method. Future work will focus on refining the synthesis process to ensure a broader applicability to various exercises. This research contributes to advancing automated physiotherapeutic exercise evaluation, highlighting the importance of synthetic data in achieving better performing and more generalizable models.

These days, many jobs like working in an office include sitting in chairs for a long time which could lead to work-related disorders such as musculoskeletal issues. Inappropriate postures can cause muscle fatigue in certain regions, resulting in pain and discomfort. Analysis of different types of postures to indicate discomfort could help us choose an optimal posture. This study evaluates five different sitting postures in an office chair for comfort and discomfort. Each posture was held for 18 minutes with a two minute break between postures. Six participants with an equal number of male and female subjects were chosen. The sitting posture correlates with the distribution of the weight on the seat, which can be measured by pressure sensors. The pressure distribution was obtained using a custom-built pressure mat and the maximum pressure were evaluated. The McGill Questionnaire construct was used to find subjective discomfort at each minute. There was no difference in results for both sexes. Overall, leaning to one side was felt more comfortable while sitting with a curved back caused the highest discomfort.

Wearable electrocardiography monitors, e.g. embedded in textile shirts, offer new approaches in diagnosis but suffers upon limited computational capacities. Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. For each type of noise a dictionary is built using K-singular value decomposition. This iterative method alternates between finding a sparse representation for every training signal and then updating every atom of the dictionary on its own. A sparse representation is found using the orthogonal matching pursuit algorithm. The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm. Electrocardiography data stems from synthetically-generated signals as well as the freely-available Brno University of Technology ECG Quality Database. Noise is added to the signals using the MIT-BIH Noise Stress Database. Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. Moreover, a small number of training data is sufficient for satisfying results which indicates the suitability of the method for wearable hardware with low memory and power specifications.

Many factors can negatively affect the effectiveness of cleaning and disinfection measures. Due to this situation, the Technical University of Amberg-Weiden in cooperation with aquagroup AG investigated a dry fogging technology as an automated system for decontamination of limited volumes. Preliminary tests in smaller rooms had already shown positive results in this context. An upscaling of the method would therefore be of interest, as it could be used to decontaminate rooms that are difficult or very costly to reprocess due to their size or layout. In order to evaluate the use of this system in operating theaters or larger rooms, a test setup was established in an aquagroup AG painting hall. In addition, thereby the effectiveness of the method can be evaluated in unprepared and unsealed rooms with high ventilation. Our results show a maximum germ reduction of over 5.7 log levels on tiles and 5.2 log levels on plastic. If further studies confirm these initial positive results, the dry fogging we are investigating could be another option for cleaning or disinfecting large areas quickly and effectively.

Administering fluids and drugs intravenously is crucial in caring for vulnerable patient cohorts such as critically ill patients as well as neonatal and paediatrics patient populations. Studies have revealed severe contamination of infusion solution that could be avoided by utilizing in-line filters. The filtration performance consequently depends on the geometry of the filter housing. The purpose of our numerical study was to analyse the flow situation in filter housings depending on the geometry (diameter of the filter housing and distance between entrance and membrane). We compared the flow of two circular filter system with different housing width (D = 25 mm; L = 1.5 mm/3.0 mm) by means of computational fluid dynamics (CFD). The filter membrane was modelled by a porous jump condition. Both filter systems showed a highly reduced inflow on the membrane compared to the velocity in the Luer Lock ports. The wide filter housing facilitates a more homogenous inflow on the membrane (>92% of the membrane area is applied within a range of 5% of the mean velocity) compared to the narrow filter housing. Despite that difference both filter housings induced a well distributed flow through the filter membrane. However, for large filter systems (>50 mm diameter) the design of the filter housing could play a crucial role in optimising filter performance and therefore CFD should be considered.

Efficient personalized ablation strategies for treating atrial arrhythmias remain challenging. Discrepancies in identifying arrhythmogenic areas using characterization methods, such as late gadolinium enhanced magnetic resonance imaging (LGE-MRI) and electroanatomical mapping, require a comparative analysis of local impedance (LI) and LGE-MRI data. This study aims to analyze correlations as basis for improvement of treatment strategies. 16 patients undergoing left atrium (LA) ablation with LGE-MRI acquisition and LI data recording were recruited. LGE-MRI data and LI measurements were normalized to patient- and modality-specific blood pool references. A global mean shape was generated based on all patient geometries, and normalized local impedance (LI N ) and LGE-MRI image intensity ratio (IIR) data points were coregistered for comparison. Data analysis comprised intra-patient and inter-patient assessments, evaluating differences in LIN values among datasets categorized by their IIR. Due to substantial deviations in LI N values, even within the same patient and IIR-category, discerning the presence or absence of a correlation was challenging, and no statistically significant correlation could be identified. Our findings underscore the necessity for standardized protocols in data acquisition, processing, and comparison, to minimize unquantified confounding effects. While immediate substitution of LI for LGE-MRI seems improbable given the significant LI N variations, this preliminary study lays the groundwork for systematic data acquisition. By ensuring data quality, a meaningful comparison between LI and LGE-MRI data can be facilitated, potentially shaping future strategies for atrial arrhythmia treatment.

Due to the recent popularity and availability of Large Language Models (LLMs), creators of educational materials can more efficiently extract keywords for use in personalised learning recommendations than ever before. However, due to the LLMs’ probabalistic nature, the automation of the otherwise labour-intense keyword extraction inherits the risk of biased and non-explainable results. In this research, we present an original framework to enhance keyword selection based on content title and description through a novel, reliability-sensitive, keyword selection algorithm. For this, we collected 38 potential keywords (together with their definitions) for five topics on dementia care from previous studies, together with two contents per topic. To assess the new method’s support in extracting keywords, we then prompted 5 human experts and 3 LLMs (using Retrieval Augmented Generation (RAG) for the keyword definitions) to select keywords to include and exclude for each content. Using Krippendorf’s a metric, we then were able to adapt to the present agreement, and to reliably select keyword sets for inclusion and exclusion for each content individually. Last, we compared these LLM-based keyword sets with those selected by humans to assess the impact of the adaptive keyword selection algorithm. Overall, the results suggest that LLMs generally struggle with the task (66% of extraction attempts either contained hallucinated or did not return any keywords), and topic-wise internal agreement is low ( a=0.59 (0.42) for model 3 (using RAG) on average; a=0.68 for human raters). Due to this, the reliable keyword selection resulted in a median set of 6|27 keywords for inclusion|exclusion per topic, with many of those keywords being within the benchmark keyword sets selected by human raters. To conclude, this approach shows effective in adapting to different levels of agreement in extracting keywords.

Unilateral leg amputees suffer from an increased risk of gonarthrosis. The risk of substantial joint damage ca be decreased by an early diagnosis, therapy and long-term monitoring to regularly assess the knee joint’s condition. To adapt the acoustic-kinetic analysis to unilateral amputees, a hand rig with Hottinger Quantum X440B and 2SMR sensors was added to the measurement setup. It helps the probands to keep their balance and to achieve a fluent motion during their test exercise of 3 knee bends in 10 seconds. The measurement setup comprises a ZebrisR system to measure the vertical ground force, two Zebris SyncR Cams to assess the sagittal knee angle, the frontal horizontal knee shift and - in case of the amputees- the data of the hand forces via the Hottinger sensors. It is crucial to establish if the test exercise is adequate for the amputees and if the probands can perform the exercise regularly and over a long period of time with less than 10% - 20% variation. To this end a case study out of a pilot study of 18 case conditions and 108 measurements is presented to clarify the approach. The proband is female, 43 years old, with a unilateral amputation at the thigh. She does not suffer from arthrosis. She performed the test routine six times over a period of five weeks. The kinetic analysis resulted in a characteristic pattern, comparable to non-amputated patients. The results showed a good recurrence and therefore indicate that the test exercise of 3 knee bends in 10 seconds with hand support will be the exercise prospective studies.

Due to promising results, the patient cohort for transcather aortic valve replacement (TAVR) has been extended in recent years to include patients with a bicuspid aortic valve (BAV). There are different types of BAV. One variant is the tricommisural bicuspid aortic valve (TBAV). BAV have an increased risk of post-TAVR complications such as paravalvular leakage. In the case of paravalvular leakage, blood flows past the prosthesis back into the ventricle during diastole. Clinically, patients with BAV are often pre-dilated. For this reason, we want to investigate how pre-dilatation of BAV can affect the leakage rate. A simplified model is used for pre-dilatation, where the calcification nodule is cut along the free edge of the leaflets before the deployment. In order to evaluate the effects of this method, a deployment simulation was carried out for both geometries using an explicit calculation. A flow simulation was then performed to determine the paravalvular leakage. The pre-dilatation allows the leaflets to move independently of each other. Without pre-dilatation, the TAVR cannot fully expanded. The leakage rate is higher for the BAV than for the pre-dilated geometry (53.1mLs −1 vs. 19.4mLs −1 ). In this model, we have shown the effect of pre-dilatation on implantation results.

In recent years, Electroencephalogram (EEG) based user authentication systems have gained significant interest as an innovative approach for identity verification. EEGs are considered to be a novel biometric attribute due to the individuality of each person’s cerebral activity patterns. This work explores the feasibility and efficiency of utilizing EEG signals, generated in response to emotional stimuli, for user authentication applications, by implementing Machine Learning (ML) and Deep Learning (DL) approaches. Support Vector Machine (SVM), Random Forest (RF) classifier and 1D Convolution Neural Network (CNN) were employed to evaluate and compare the performance of EEG-based user authentication for two publicly available EEG datasets, namely DEAP and DENS database. The performance of EEGbased user authentication was significantly high in LAHV emotional state for DENS dataset, achieving an accuracy of 99.2 % and 92.59 % with SVM and modified 1D CNN, respectively.

With the PRIS-Tool we aim to enable a fast, simple and stable eye self-screening that makes pathological changes in the retinal blood vessels visible to the patient. Furthermore, we envision its application as a quick retinal function test prior to cataract surgeries in low-income regions of the world. Our pocket-sized PRIS-Tool allows for a simplified entoptic testing, due to its integrated light stimulation. PRIS stands for Purkinje Retinal Image Stabilizer. So far, we have built 12 devices in small series and distributed them in our PRIS clinical research network to discuss potential clinical applications. An assisting app will support patients in performing a reliable self-diagnosis and documenting the progress of the disease. Future work will focus on app development and the initiation and evaluation of user studies.

One of the major challenges in using magnetic nanoparticles in biomedical applications is still the accurate steering and trapping of these particles at a desired location, like at a tumor. Halbach arrays are well-established here as they generate the strongest magnetic force. However, these arrays are mechanically unstable, and thus, their potential to attract magnetic particles is often investigated fully numerically. Hence, this paper provides instructions on how to easily produce Halbach arrays using permanent magnets and metal glue. In total, three Halbach arrays of five, seven, and nine magnets were produced and analyzed regarding their potential for capturing magnetic particles in a background flow, numerically using COMSOL Multiphysics and in measurements. Furthermore, the Halbach arrays are compared with two other array configurations with all magnetizations in the same directions and alternating once. Overall, the simulation results were in good agreement with the measurement results. The amount of captured particles was significantly higher in measurements for the Halbach arrays with 8.2 %, compared to the array with alternating magnetization direction with 6.6% captured particles. Therefore, using Halbach arrays to capture or steer magnetic nanoparticles in a background flow is strongly recommended.

Nursing care jobs are predominantly described as burdensome. In particular, the workload and the time available to deal with it are in a state of imbalance. Nurses are very often under time pressure and have to make decisions under psychological, emotional and physical stresses. Machine learning methods and in particular, Decision Support Systems (DSS) can be used to support nurses with improved information (e.g. visualization of individual health risks) for decisions that are not easy to make. As falls are one of the most common health problems in care facilities, we will present a concept of a DSS for the prevention of falls that was developed in the PFLIP research project. We explain our user-centered design process based on ISO 9241-210-2019. User feedback is obtained at each phase so that nurses can identify problems at an early stage. The result is a conceptual design, visualized as a click dummy, to identify individual fall risks and predict preventive measures to reduce the risk of falling.

Active leg prostheses can provide individuals with an amputation with motion support and thereby enable better participation in societal life. However, their versatility of use is still constrained, particularly with respect to volitional control by the user. Promising volitional control methods like employing muscle and joint model simulations to mimic human motor control are actively researched. However, experimental paradigms currently either include costly hardware setups or are limited to very simplistic virtual setups. For rapid control prototyping and facilitating the involvement of healthy participants, we propose an immersive virtual reality environment, focusing on ankle prostheses. Based on a theoretical discussion and a pilot study, we outline the potential of this method for agile development of lower limb volitional controllers.

Monocular depth estimation is an important topic in minimally invasive surgery, providing valuable information for downstream application, like navigation systems. Deep learning for this task requires high amount of training data for an accurate and robust model. Especially in the medical field acquiring ground truth depth information is rarely possible due to patient security and technical limitations. This problem is being tackled by many approaches including the use of synthetic data. This leads to the question, how well does the synthetic data allow the prediction of depth information on clinical data. To evaluate this, the synthetic data is used to train and optimize a U-Net, including hyperparameter tuning and augmentation. The trained model is then used to predict the depth on clinical image and analyzed in quality, consistency over the same scene, time and color. The results demonstrate that synthetic data sets can be used for training, with an accuracy of over 77% and a RMSE below 10mm on the synthetic data set, do well on resembling clinical data, but also have limitations due to the complexity of clinical environments. Synthetic data sets are a promising approach allowing monocular depth estimation in fields with otherwise lacking data.

Contactless measurement methods offer a novel approach to assessing respiratory parameters. This study investigates the feasibility of classifying chronic obstructive pulmonary disease, asthma, and healthy individuals using depth-based plethysmography (DPG). The approach involves calculating Pearson's correlation coefficient for all pixel-wise signals against each other, with the cumulative result visualized in patient-specific masks. A convolutional neural network is used for the classification process. For evaluation, on a recorded data set (N=53), a classification accuracy of 57.7% and Cohen’s Kappa of 0.28 were reached. These findings provide indications that DPG might effectively classify respiratory conditions by analyzing respiratory motion dynamics.

Electrical Impedance Tomography is used to image the cross-sectional conductivity of an object. It is clinically used for high-frame-rate imaging of lung ventilation. Most current systems use very limited current injection wave-forms and patterns. We have developed a flexible system for researching alternatives. This paper covers our current supply module's design and characterisation. We tested the input filtering, Bandwidth, and parasitic capacitance. The imput fitering functioned similarly to the simulation of the design. However, the bandwich was limited to approximately 1 MHz instead of the desired 10 MHz due to excessive parasitic capacitance. We proposed points to redesign that should reduce the capacitance by a factor of 10 and correspondingly increase dandwidth tenfold to 10 MHz.

Electrical Impedance Tomography is used to image the cross-sectional conductivity of an object. It is clinically used for high-frame-rate imaging of lung ventilation. Most current systems use very limited current injection waveforms and patterns. We have developed a flexible system for researching alternatives. This paper covers our voltage conditioning module’s design and characterisation. The device was shown to work up to the desired 10 MHz. The responses were uniform with minor gain mismatch between all channels after manufacturing variations. The low-frequency (<1 MHz) crosstalk is on the order -55 dB, and the worst cases around 8 MHz are around -35 dB.

Machine learning algorithms and neural networks have recently been used for the classification of middle ear disorders using wideband acoustic immittance and wideband tympanometry data. This study applies the extreme gradient boosting (XGB) classifier, trained on simulated WAI data, to classify real measured data for normal, otosclerotic, and disarticulated ears. The achieved macro recall of 82 % is comparable to other approaches trained with real measurement data. The interpretability methods LIME and SHAP are used to quantify each feature’s contribution, both revealing energy reflectance between 600-800 Hz as a key feature for all classes. The key feature identified matches the differences that can be visually observed in the training and test data. However, the obtained feature contributions don’t provide enough distinguishable information to recognise incorrect or uncertain classifications.

In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyperspectral data in the range of 400 − 1000 nm is used in this work. The acquisition system consisted of two multispectral snapshot cameras creating a stereo-HSI-system. For the analysis, 27 images with annotations of glandular tissue, nerve, muscle, skin and vein in 18 patients undergoing parotidectomy are included. Three patients are removed for evaluation following the leave-one-subjectout principle. The remaining images are used for training, with the data randomly divided into a training group and a validation group. In the validation, an overall accuracy of 98.7% is achieved, indicating robust training. In the evaluation on the excluded patients, an overall accuracy of 83.4% has been achieved showing good detection and identification abilities. The results clearly show that it is possible to achieve robust intraoperative tissue differentiation using hyperspectral imaging. Especially the high sensitivity in parotid or nerve tissue is of clinical importance. It is interesting to note that vein was often confused with muscle. This requires further analysis and shows that a very good and comprehensive data basis is essential. This is a major challenge, especially in surgery.

The unified Parkinson’s disease rating scale, used to monitor the disease progression, is based on visual assessments of motor symptoms. Vocal manifestations of Parkinson’s disease differ from the motor ones, specifically in their rate of change with disease severity. As such, a different scale is needed to provide the voice measures of the disease severity. This study employed a dataset of voice-quality features from repeated recordings of Parkinson’s disease patients. The changes of all voice features across the categories were evaluated using one-way analysis-of-variance and support vector regression. Significant changes and marked non-linearly increasing or decreasing trends were shown for all features, for the three-categories scale. Significant changes and trends were obtained in the 12-categories scale, but only for the mild category and the severe category range of scores. The findings imply a potential for voice-based monitoring for the early and late severity stages of Parkinson’s disease that could be continuously used by patients and provide timely warnings of deterioration.

The quality of remote photoplethysmography (rPPG) signals presents a significant challenge in contactless optical blood pressure measurement. Feature and morphologybased approaches heavily rely on subtle changes in signal characteristics, but rPPG signals are highly susceptible to noise and interference. This study aims to evaluate rPPG signal quality by assessing correlation with a reference PPG and signal-to-noise ratio (SNR) across various color models and rPPG methods. Analyses are performed under different measurement conditions, accounting for common sources of signal artifacts.

The technique of 3D-printed micro-optics has experienced significant advancements over the last few years [1, 2]. This progressive evolution has been marked by remarkable strides in precision and miniaturization, making it particularly attractive to applications in the field of medical endoscopy. One noteworthy application entails the fabrication of micro-lenses directly on optical fibers, thereby creating endoscopic devices with unparalleled capabilities. A significant attribute of these micro-optical systems lies in their capability for precise imaging and their adept navigation through exceptionally narrow biological structures. For instance, the application of the 3D-printed endoscopy technology has facilitated insertion into the aorta of mice, as well as severely diseased human carotid arteries [2, 3]. This unprecedented level of accessibility and adaptability holds immense promise for diagnostic and therapeutic interventions in the realm of cardiovascular medicine and beyond. In the future, it may also be conceivable for micro-lenses to serve not only as imaging tools, but also as integral components of 3D-printing processes aimed at repairing damaged human tissue with bio-materials. For micro-lens production and quality control, performance assessment is invariably necessary to assure diffraction limited imaging in the different optical applications. With confocal surface profiling, only the surface shape information of micro-optics can be obtained. However, not only the shape of 3D-printed optics is relevant for its quality, but also its refractive index distribution is crucial when determining its overall optical performance. Therefore, the measurement of the wavefront is decisive in evaluating lens performance. Lateral shear interferometry presents itself as a compact and precise method for wavefront measurements. The only essential component required in the set-up is a pair of glass plates with high surface quality. Consequently, the entire set-up can be notably streamlined, as there is no requirement to construct an interferometer reference arm. This advantage facilitates the development of a notably compact set-up, making lateral shear interferometry particularly advantageous for integration with microscope and 3D-printer, despite the requirement for more complex algorithms in analyzing lateral shear interferograms. In this paper, simulation of shear interferogram fringes corresponding to wavefronts exhibiting varying aberrations are discussed. Additionally, this research encompasses measurement and analysis of shear interferograms obtained from wave fronts transmitted through different lenses. Through meticulous examination of these experimental results, the characteristics and performance of the tested optical systems are elucidated, contributing to a deeper understanding of wavefront analysis methodologies and their applicability in optical characterization studies.