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

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. Howev...

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Autores principales: Hwang, Eui Jin, Kim, Ki Beom, Kim, Jin Young, Lim, Jae-Kwang, Nam, Ju Gang, Choi, Hyewon, Kim, Hyungjin, Yoon, Soon Ho, Goo, Jin Mo, Park, Chang Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184006/
https://www.ncbi.nlm.nih.gov/pubmed/34097708
http://dx.doi.org/10.1371/journal.pone.0252440
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author Hwang, Eui Jin
Kim, Ki Beom
Kim, Jin Young
Lim, Jae-Kwang
Nam, Ju Gang
Choi, Hyewon
Kim, Hyungjin
Yoon, Soon Ho
Goo, Jin Mo
Park, Chang Min
author_facet Hwang, Eui Jin
Kim, Ki Beom
Kim, Jin Young
Lim, Jae-Kwang
Nam, Ju Gang
Choi, Hyewon
Kim, Hyungjin
Yoon, Soon Ho
Goo, Jin Mo
Park, Chang Min
author_sort Hwang, Eui Jin
collection PubMed
description 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.
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spelling pubmed-81840062021-06-21 COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system Hwang, Eui Jin Kim, Ki Beom Kim, Jin Young Lim, Jae-Kwang Nam, Ju Gang Choi, Hyewon Kim, Hyungjin Yoon, Soon Ho Goo, Jin Mo Park, Chang Min PLoS One Research Article 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. Public Library of Science 2021-06-07 /pmc/articles/PMC8184006/ /pubmed/34097708 http://dx.doi.org/10.1371/journal.pone.0252440 Text en © 2021 Hwang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hwang, Eui Jin
Kim, Ki Beom
Kim, Jin Young
Lim, Jae-Kwang
Nam, Ju Gang
Choi, Hyewon
Kim, Hyungjin
Yoon, Soon Ho
Goo, Jin Mo
Park, Chang Min
COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
title COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
title_full COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
title_fullStr COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
title_full_unstemmed COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
title_short COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
title_sort covid-19 pneumonia on chest x-rays: performance of a deep learning-based computer-aided detection system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184006/
https://www.ncbi.nlm.nih.gov/pubmed/34097708
http://dx.doi.org/10.1371/journal.pone.0252440
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