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COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system

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Author(s)
Eui Jin HwangKi Beom KimJin Young KimJae-Kwang LimJu Gang NamHyewon ChoiHyungjin KimSoon Ho YoonJin Mo GooChang Min Park
Keimyung Author(s)
Kim, Jin Young
Department
Dept. of Radiology (영상의학)
Journal Title
PLoS One
Issued Date
2021
Volume
16
Issue
6
Abstract
Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss' kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.
Keimyung Author(s)(Kor)
김진영
Publisher
School of Medicine (의과대학)
Citation
Eui Jin Hwang et al. (2021). COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system. PLoS One, 16(6), e0252440. doi: 10.1371/journal.pone.0252440
Type
Article
ISSN
1932-6203
Source
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252440
DOI
10.1371/journal.pone.0252440
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43651
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Radiology (영상의학)
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