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 for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning

The virus new variants of Coronavirus disease 2019 (COVID-19) continue to appear, making the situation more challenging and threatening. The COVID-19 pandemic has profoundly affected health systems and medical centres worldwide. The primary clinical tools used in diagnosing patients presenting with respiratory distress and suspected COVID-19 symptoms are radiology examinations. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical specialists. This paper presents an Augmented Reality (AR) tool for COVID-19 aid diagnosis, including Computerised Tomography Ct-scans segmentation based Deep Learning, 3D reconstruction, and AR visualisation. Segmentation is a critical step in AI-based COVID-19 image processing and analysis;we use the popular segmentation networks, including classic U-Net. Quantitative and qualitative evaluation showed reasonable performance of U-Net for lung and COVID-19 lesions segmentation. The AR-COVID-19 aid diagnosis system could be used for medical education professional training and as a support visualisation and reading tool for radiologist. © 2022 IEEE.