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Year : 2022  |  Volume : 5  |  Issue : 1  |  Page : 19-25

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

1 Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
2 Endimension Technology Pvt. Ltd., Thane, Maharashtra, India
3 Kaushalya Medical Foundation, Thane, Maharashtra, India
4 Jupiter Hospital, Thane, Maharashtra, India

Correspondence Address:
Abhishek Mahajan
Fellowship in Cancer Imaging, MRes (KCL, London), FRCR (UK), Consultant Radiologist, The Clatterbridge Cancer Centre NHS Foundation Trust, Pembroke Place, Liverpool, L7 8YA
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/crst.crst_162_21

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Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objectives: In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Materials and Methods: The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Results: We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion: Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.

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