Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information.

In this paper, software architecture model “MVC-3DC” for Collaborative Augmented and Virtual Reality Systems design is proposed. This model is the results of merging several aspects: Human-Computer Interaction (HCI), distribution systems, computer-supported cooperative work (CSCW) and new technologies such as augmented reality and virtual reality. MVC-3DC integrates collaboration principles between remote users. MVC-3DC allows a low dependency between components such as the core functions, 3D graphics API and data distribution modes. The proposed architectural model integrates simulation models, SDKs and algorithms for different nodes involved in a collaborative session. This facilitates interoperability and capability to manage heterogeneity and relationship between different nodes participating in the collaborative session. Finally, our model makes it possible to integrate other toolkits without completely changing the structure of collaboration model. A simple adaptation could be made.

In recent decades, virtual reality (VR) becomes a potential solution to enhance clinical medical (functional reeducation, training, etc.), especially with the growth evolution of technologies form both visualization (e.g., HoloLens, VR in Case, etc.) and 3D gestural interaction (Ray Casting, Free Hand, etc.) point of views. The 3D visualization of the human anatomy could be a serious asset for students in medicine. This new technology could provide a clear and realistic representation of the internal organs of the human body, without having to resort to surgery. 3D organs based-course supports visualization could be a useful tool for students, especially in their first graduate studies, to enhance their perception on human’s internal composition. This system is composed of two modules, 3D human’s anatomy visualization module and interaction module for organs manipulation. Finally, the system will be tested and evaluated with several subjects.