ABSTRACT
Introduction: Computed Tomography (CT) is rapid and sensitive enough to identify COVID-19 pneumonia in its early stages. But because of the disease's high case load, it is difficult for the talented radiologists to report the cases. Therefore, using Artificial Intelligence (AI) to support radiologists' work will be crucial for producing prompt and precise results. Objective: To determine diagnostic effectiveness of AI in identifying different COVID-19 CT patterns and to correlate the AI findings with the findings appreciated by skilled Radiologists. Material and Methods: A prospective study consisting of 500 patients with RT-PCR positive COVID- 19 patients were evaluated, after obtaining informed consent. Data was analysed and represented in the form of frequencies and proportions. Collected data were analysed by Pearson's correlation coefficient (r), Intra Class Correlation (ICC) coefficient, Bland--Altman analysis. Results: AI can assess the severity of disease quickly and with good accuracy compared to manual analysis by decreasing the time taken to analyse the scan by 50%, and overall accuracy of approximately 90%. Conclusion: We conclude that as manual analysis of Chest CT in COVID-19 high case load scenario is comparatively more time-consuming, there is a need for a quick, accurate, and automated technique for identification and quantification of common findings in COVID-19. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)