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KMID : 0385920210320030249
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Journal of the Korean Society of Emergency Medicine
2021 Volume.32 No. 3 p.249 ~ p.256
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Application of deep learning algorithm to detect COVID-19 pneumonia in chest X-ray
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Jang Se-Bum
Chung Han-Sol
Park Sin-Yul
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Abstract
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Objective: This study evaluated the deep learning (DL) algorithm performance to detect lesions that suggest pneumonia in chest X-rays (CXR) of suspected coronavirus disease 2019 (COVID-19) patients.
Methods: This retrospective study included consecutive patients who visited a screening clinic in Daegu, and were suspected to be afflicted with the COVID-19 during the COVID-19 epidemic. CXR were analyzed using the commercial artificial intelligence product that provides free online DL algorithms to the public for COVID-19. Computerized tomography was used as the standard reference. Performance of the DL algorithm was evaluated by the sensitivity and specificity, and results were compared to the CXR records of emergency physicians (EP) in charge of the actual screening triage clinic during the COVID-19 epidemic.
Results: Totally, 114 patients were evaluated, of which 38 patients were positive for COVID-19. In 85 CXRs examined (36 COVID-19 and 49 non-COVID-19) with findings of pneumonia in computerized tomography, the DL algorithm showed significantly higher sensitivity as compared to the EP (DL, 98.8% [93.6%-99.9%] vs. EP, 85.9% [76.6%-92.5%]; P<0.01). Moreover, the DL algorithm showed significantly higher sensitivity for detecting CXRs with COVID-19 pneumonia, as compared to the EP (DL, 100.0% [90.3%-100%] vs. EP, 91.7% [77.5%-98.3%]; P=0.08).
Conclusion: We conclude that for examining the CXR of patients with suspected COVID-19, sensitivity of the DL algorithm is superior than the EP for detecting lesions suggesting pneumonia. Thus, the application of the DL algorithm is potentially useful in screening triage clinics to detect COVID-19 pneumonia.
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KEYWORD
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Deep learning, COVID-19, Screening triage, COVID-19 diagnostic testing, Radiography
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