Creation of Virtual Reality for Education Purposes

Virtual reality systems have been developed primarily for the entertainment sector. However, they are being increasingly considered as high potential tools for use in industry and education. In this context, schools are now facing a challenge to introduce virtual-reality-supported teaching into their processes. With this in mind, the authors, in their paper, focus on the possibility for using virtual excursions as part of vocational education and training. For this purpose, they analyze the suitability and usability of selected virtual reality systems, as well as relevant camera systems, for the creation of virtual reality software products designed for industrial practice in upper secondary vocational schools' apprenticeships (vocational education and training). The main results of their analyses are summarized in the form of tabularized SWOT parameters.

Augmented Reality for medical practice: A comparative study of Deep learning models for Ct-scan segmentation

The coronavirus disease has hardly affected medical healthcare systems worldwide. Physicians use radiological examinations as a primary clinical tool for diagnosing patients with suspected COVID-19 infection. Recently, deep learning approaches have further enhanced medical image processing and analysis, reduced the workload of radiologists, and improved the performance of radiology systems. This paper addresses medical image segmentation;we present a comparative performance study of four neural networks 'NN' models, U-Net, 3D-Unet, KiU-Net and SegNet, for aid diagnosis. Additionally, we present his 3D reconstruction of COVID-19 lesions and lungs and his AR platform with augmented reality, including AR visualization and interaction. Quantitative and qualitative assessments are provided for both contributions. The NN model performed well in the AI-COVID-19 diagnostic process. The AR-COVID-19 platform can be viewed as an ancillary diagnostic tool for medical practice. It serves as a tool to support radiologist visualization and reading. © 2022 IEEE.

Augmented Reality: The Emerging Technology in Medical Field

Augmented Reality (AR) has visualized remarkable growth over the past few decades and currently it is ready to be utilized in various electronic devices such as personal computer, laptops, mobile phones, tablets, etc. At present, this recent technology has discovers its association in several different sectors with its intelligent and smart software which can be easily utilized in various sectors such as medical, education and many more. The increasing demand for Augmented Reality devices and applications in healthcare, rising investments in AR market, and growing demand for ARin education sectors due to COVID-19 are the key factors driving the AR market growth.AR is a new step in enhancement of technology where a user can feel virtual objects in real physical world. With the introduction of this technology in education sector teachers can pass on their insight on a particular topic more vitally, effectively and in an alluring way. Students will be able to understand things more preciously and easily if this innovation is acquainted with them. This paper reviews the improvement in the current education and learning system by introducing the concept of AR. © 2022 American Institute of Physics Inc.. All rights reserved.

COVID-19 geoviz for spatio-temporal structures detection

The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic's spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends.

Introduction

Introducing Information Technology (IT) tools in today’s and future medicine is being increasingly considered all over the world, especially in developed countries. Indeed, these countries are suffering the most from population aging and chronic diseases. This induces original challenges not only for healthcare but also for wellbeing. In this sense, Predictive, Preventive and Personalised Medicine (3PM) has been identified since more than a decade as a research avenue having huge potential for developed societies. © Springer Nature Switzerland AG 2020.

A reverse form of Linburg-Comstock variation with comments on its etiology and demonstration of interactive 3D portable document format.

PURPOSE: Two most common variations of flexor pollicis longus include its accessory head and its connection with the flexor digitorum profundus of the index (Linburg-Comstock variation). In addition, while three-dimensional (3D) screening has widely been used in anatomical education, its use as reporting tool in anatomical research is still limited. The objective of this study is to report a previously unrecognized form of the accessory head of flexor pollicis longus, discuss the potential etiology of Linburg-Comstock variation, and pilot the 3D scanning of a large-scale anatomical structure. METHODS: An unusual tendon slip was discovered during a routine dissection in the anterior compartment of the right forearm of a 54-year-old male cadaver. A 3D scanner was used to capture the surface topography of the specimen and an interactive portable document format (PDF) was created. RESULTS: An anomalous tendon was found originating from the lateral aspect of the flexor digitorum profundus muscle. This variant tendon then inserted onto the medial surface of the flexor pollicis longus tendon before entering the carpal tunnel. The variation resembles a reverse form of Linburg-Comstock variation, because pulling this variant tendon resulted in simultaneous flexion of the interphalangeal joint of thumb. CONCLUSION: Surgeons should be aware of the reverse Linburg-Comstock variation, because it may not be detectable by the conventional provocative testing. Linburg-Comstock variation may be classified as an anatomical variant or a secondarily acquired condition depending on its type. Our demonstration of interactive 3D-PDF file highlights its potential use for delivering anatomical information in future cadaveric studies.

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Background: Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus. Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing. However, because of the present lockdown measures in several countries, the validation of computer vision systems using large-scale datasets is a challenge. Methods: In this paper, a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality (VR) technology. Using VR, we modeled a digital twin (DT) of an existing office space and used it to create a dataset of individuals in different postures, dresses, and locations. To test the proposed solution, we implemented a convolutional neural network (CNN) model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures. Results: We detected the number of persons in both the real and synthetic datasets with more than 90% accuracy, and the actual and measured distances were significantly correlated (r=0.99). Finally, we used intermittent-layer- and heatmap-based data visualization techniques to explain the failure modes of a CNN. Conclusions: A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals. The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed. © 2021 Beijing Zhongke Journal Publishing Co. Ltd

Visualization Process of Temporary Converted Facilities to Accommodate Quarantine and Treatment Centre (PKRC) Layouts in Malaysia Agriculture Expo Park Serdang (MAEPS)

This study aims to develop a reference platform for converting Malaysia Agriculture Expo Park Serdang (MAEPS) to the Low-Risk COVID-19 Quarantine and Treatment Centre (PKRC) to face the increased numbers Covid-19. This study employed qualitative methodologies and further developed 3D modeling involving AutoCAD, SketchUp, and V-Ray software. The limitation is developing a 3D model visualization of MAEPS on Phase 1 and Phase 2 at Hall A only. The findings display the application of 3D visualization potentially becomes a reference to creating the quarantine centre in the future.

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images.

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.