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In Journal of medical virology

Coronavirus Disease 2019 (COVID-19) has quickly turned into a global health problem. Computed Tomography (CT) findings of COVID-19 pneumonia and community-acquired pneumonia (CAP) may be similar. Artificial intelligence (AI) is a popular topic among medical imaging techniques and has caused significant developments in diagnostic techniques. This retrospective study aims to analyze the contribution of AI to the diagnostic performance of pulmonologists in distinguishing COVID-19 pneumonia from CAP using CT scans. A deep learning-based AI model was created to be utilized in the detection of COVID-19, which extracted visual data from volumetric CT scans. The final dataset covered a total of 2496 scans (887 patients), which included 1428 (57.2%) from the COVID-19 group and 1068 (42.8%) from the CAP group. CT slices were classified into training, validation, and test datasets in an 8:1:1. The independent test dataset was analyzed by comparing the performance of four pulmonologists in differentiating COVID-19 pneumonia both with and without the help of the AI. The accuracy, sensitivity, and specificity values of the proposed AI model for determining COVID-19 in the independent test dataset were 93.2%, 85.8%, and 99.3%, respectively, with the area under the ROC curve of 0.984. With the assistance of the AI, the pulmonologists accomplished a higher mean accuracy (88.9% vs 79.9%,p<0.001), sensitivity (79.1% vs 70%, p<0.001), and specificity (96.5% vs 87.5%,p<0.001). AI support significantly increases the diagnostic efficiency of pulmonologists in the diagnosis of COVID-19 via CT. Studies in the future should focus on real-time applications of AI to fight the COVID-19 infection. This article is protected by copyright. All rights reserved.

İn Erdal, Altıntop Geçkil Ayşegül, Kavuran Gürkan, Şahin Mahmut, Berber Nurcan Kırıcı, Kuluöztürk Mutlu

2022-Apr-13

Artificial Intelligence (AI), Community-Acquired Pneumonia, Computed Tomography (CT), Coronavirus Disease 2019 (COVID-19), Deep Learning