Volume 16, Issue 3

Immersive technologies, like Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) have undergone technical evolutions over the last few decades. Their rapid development and dynamic changes enable their effective applications in medicine, in fields like imaging, preprocedural planning, treatment, operations planning, medical students training, and active support during therapeutic and rehabilitation procedures. Within this paper, a comprehensive analysis of VR/AR/MR application in the medical industry and education is presented. We overview and discuss our previous experience with AR/MR and 3D visual environment and MR-based imaging systems in cardiology and interventional cardiology. Our research shows that using immersive technologies users can not only visualize the heart and its structure but also obtain quantitative feedback on their location. The MR-based imaging system proposed offers better visualization to interventionists and potentially helps users understand complex operational cases. The results obtained suggest that technology using VR/AR/MR can be successfully used in the teaching process of future doctors, both in aspects related to anatomy and clinical classes. Moreover, the system proposed provides a unique opportunity to break the boundaries, interact in the learning process, and exchange experiences inside the medical community.

Objectives Cardiac auscultation remains a crucial part of physical examination. In preclinical training, there are multiple approaches for teaching that skill. Our goal was to find a compromise between expensive and complicated high-fidelity simulators and simple devices with a lack of realism. Methods Our project is made up of three main parts: a manikin’s torso, a specially prepared stethoscope, and a smartphone application. The position of the stethoscope’s head is recognized by Hall effect sensors inside of a manikin, and the information is sent via Bluetooth to the smartphone. Data are interpreted by the application, and proper recording is selected from sounds’ base. The user can easily adjust additional settings (e.g., main volume, playback speed, background noises). Then, the processed sound is played via a Bluetooth headset that is a part of the stethoscope. Results The solution that we suggest is easy to use with minimal adversely affecting the quality of learning. Handling of our device is intuitive, and minimal prior training is required. The low cost of the device itself and the widespread use of smartphones make it easy to implement. Conclusions We believe that this solution could be a complement for the currently used methods for teaching cardiac auscultation in preclinical training.

Objectives The electroencephalographic signal is largely exposed to external disturbances. Therefore, an important element of its processing is its thorough cleaning. Methods One of the common methods of signal improvement is the independent component analysis (ICA). However, it is a computationally expensive algorithm, hence methods are needed to decrease its execution time. One of the ICA algorithms (fastICA) and parallel computing on the CPU and GPU was used to reduce the algorithm execution time. Results This paper presents the results of study on the implementation of fastICA, which uses some multi-core architecture and the GPU computation capabilities. Conclusions The use of such a hybrid approach shortens the execution time of the algorithm.

Objectives In this paper a novel approach regarding image analysis in Machine Vision applications was proposed. Methods The presented concept consists of two issues: (1) shifting some of the complex image processing and understanding algorithms from a mobile robot to distributed computer, and (2) designing the cognitive system (in a distributed computer) in such a way, that it would be common for numerous robots. The authors of this work focused on image processing, and they propose to accelerate vision understanding by using Cooperative Vision (CoV), i.e., to get video input from cooperating robots and process it in a centralized system. Results To verify the purposefulness of such approach, a comparative study is currently being conducted, involving a classical single-camera Computer Vision (CV) mobile robot and two (or more) single-camera CV robots cooperating in CoV mode. Conclusions The CoV system is being designed and implemented so that the algorithm would be able to utilize multiple video sources for recognition of objects on the very same scene.

Introduction Chronic obstructive pulmonary diseases are the most common disease worldwide. Asthma and sleep apnea are the most prevalent of pulmonary diseases. Patients with such chronic diseases require special care and continuous monitoring to avoid any respiratory deterioration. Therefore, the development of a dedicated and reliable sensor with the aid of modern technologies for measuring and monitoring respiratory parameters is very necessary nowadays. Objective This study aims to develop a small and cost-effective respiratory rate sensor. Methods A microcontroller and communication technology (NodeMCU) with the ThingSpeak platform is used in the proposed system to view and process the respiratory rate data every 60 s. The total current consumption of the proposed sensor is about 120 mA. Four able-bodied participants were recruited to test and validate the developed system. Results The results show that the developed sensor and the proposed system can be used to measure and monitor the respiratory rate. Conclusions The demonstrated system showed applicable, repeatable, and acceptable results.

The printed character recognition is an efficient and automatic method for inputting information to a computer nowadays that is used to translate the printed or handwritten images into an editable and readable text file. This paper aims to recognize a multifont and multisize of the English language printed word for a smart pharmacy purpose. The recognition system has been based on a convolution neural network (CNN) approach where line, word, and character are separately corrected, and then each of the separated characters is fed into the CNN algorithm for recognition purposes. The OpenCV open-source library has been used for preprocessing, which can segment English characters accurately and efficiently, and for recognition, the Keras library with the backend of TensorFlow has been used. The training and testing data sets have been designed to include 23 different fonts with six different sizes. The CNN algorithm achieves the highest accuracy of 96.6% comparing to the other state-of-the-art machine learning methods. The higher classification accuracy of the CNN approach shows that this type of algorithm is ideal for the English language printed word recognition. The highest error rate after testing the system using English electronic prescribing written with all proposed font-types is 0.23% in Georgia font.

Deconvolution microscopy is a very useful, software-based technique allowing to deblur microscopy images and increase both lateral and axial resolutions. It can be used along with many of fluorescence microscopy imaging techniques. By increasing axial resolution, it also enables three-dimensional imaging using a basic wide-field fluorescence microscope. Unfortunately, commercially available deconvolution software is expensive, while freely available programs have limited capabilities of a batch file processing. In this work we present BatchDeconvolution , a Fiji plugin that bridges two programs that we used subsequently in an image deconvolution pipeline: PSF Generator and DeconvolutionLab2 , both from Biomedical Imaging Group, EPFL. Our software provides a simple way to perform a batch processing of multiple microscopy files with minimal working time required from the user.