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Year : 2021  |  Volume : 4  |  Issue : 2  |  Page : 256-261

Novel artificial intelligence algorithm for automatic detection of COVID-19 abnormalities in computed tomography images

1 Endimension Technology Private Limited (Incubator Under SINE IIT Mumbai), Mumbai, Maharashtra, India
2 Department of Radiodiagnosis, Tata, Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India

Correspondence Address:
Abhishek Mahajan
Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai - 400 012, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/crst.crst_28_21

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Background: Chest computed tomography (CT) is a readily available diagnostic test that can aid in the detection and assessment of the severity of the coronavirus disease 2019 (COVID-19). Given the wide community spread of the disease, it can be difficult for radiologists to differentiate between COVID-19 and non-COVID-19 pneumonia, especially in the oncological setting. Objective: This study was aimed at developing an artificial intelligence (AI) algorithm that could automatically detect COVID-19-related abnormalities from chest CT images and could serve as a diagnostic tool for COVID-19. In addition, we assessed the performance and accuracy of the algorithm in differentiating COVID-19 from non-COVID-19 lung parenchyma pathologies. Materials and Methods: A total of 1581 chest CT images of individuals affected with COVID-19, individuals affected with non-COVID-19 pathologies, and healthy individuals were included in this study. All the digital images of COVID-19-positive cases were obtained from web databases available in the public domain. About 60% of the data were used for training and validation of the algorithm, and the remaining 40% were used as a test set. A single-stage deep learning architecture based on the RetinaNet framework was used as the AI model for image classification. The performance of the algorithm was evaluated using various publicly available datasets comprising patients with COVID-19, patients with pneumonia, other lung diseases (underlying malignancies), and healthy individuals without any abnormalities. The specificity, sensitivity, and area under the receiver operating characteristic curve (AUC) were measured to estimate the effectiveness of our method. Results: The semantic and non-semantic features of the algorithm were analyzed. For the COVID-19 classification network, the sensitivity, specificity, accuracy, and AUC were 0.92 (95% confidence interval [CI]: 0.85–0.97), 0.995 (95% CI: 0.984–1.0), 0.972 (95% CI: 0.952–0.988), and 0.97 (95% CI: 0.945–0.986), respectively. For the non-COVID classification network, the sensitivity, specificity, and accuracy were 0.931 (95% CI: 0.88–0.975), 0.94 (95% CI: 0.90–0.974), and 0.935 (95% CI: 0.90, 0.965), respectively. Conclusion: The AI algorithm developed in our study can detect COVID-19 abnormalities from CT images with high sensitivity and specificity. Our AI algorithm can be used for the early detection and timely management of patients with COVID-19.

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