RESUMO
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.
RESUMO
Coronavirus disease (COVID-19) is a severe infectious disease that causes respiratory illness and has had devastating medical and economic consequences globally. Therefore, early, and precise diagnosis is critical to control disease progression and management. Compared to the very popular RT-PCR (reverse-transcription polymerase chain reaction) method, chest CT imaging is a more consistent, sensible, and fast approach for identifying and managing infected COVID-19 patients, specifically in the epidemic area. CT images use computational methods to combine 2D X-ray images and transform them into 3D images. One major drawback of CT scans in diagnosing COVID-19 is creating false-negative effects, especially early infection. This article aims to combine novel CT imaging tools and Virtual Reality (VR) technology and generate an automatize system for accurately screening COVID-19 disease and navigating 3D visualizations of medical scenes. The key benefits of this system are a) it offers stereoscopic depth perception, b) give better insights and comprehension into the overall imaging data, c) it allows doctors to visualize the 3D models, manipulate them, study the inside 3D data, and do several kinds of measurements, and finally d) it has the capacity of real-time interactivity and accurately visualizes dynamic 3D volumetric data. The tool provides novel visualizations for medical practitioners to identify and analyze the change in the shape of COVID-19 infectious. The second objective of this work is to generate, the first time, the CT African patient COVID-19 scan datasets containing 224 patients positive for an infection and 70 regular patients CT-scan images. Computer simulations demonstrate that the proposed method's effectiveness comparing with state-of-the-art baselines methods. The results have also been evaluated with medical professionals. The developed system could be used for medical education professional training and a telehealth VR platform.
