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Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines

Background: Computer-aided detection (CAD) of pulmonary tuberculosis (TB) and silicosis among ex-miners from the South African gold mines has the potential to ease the backlog of lung examinations in clinical screening and medical adjudication for miners’ compensation. This study aimed to determine...

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Autores principales: Ehrlich, Rodney, Barker, Stephen, te Water Naude, Jim, Rees, David, Kistnasamy, Barry, Naidoo, Julian, Yassi, Annalee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565035/
https://www.ncbi.nlm.nih.gov/pubmed/36231700
http://dx.doi.org/10.3390/ijerph191912402
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author Ehrlich, Rodney
Barker, Stephen
te Water Naude, Jim
Rees, David
Kistnasamy, Barry
Naidoo, Julian
Yassi, Annalee
author_facet Ehrlich, Rodney
Barker, Stephen
te Water Naude, Jim
Rees, David
Kistnasamy, Barry
Naidoo, Julian
Yassi, Annalee
author_sort Ehrlich, Rodney
collection PubMed
description Background: Computer-aided detection (CAD) of pulmonary tuberculosis (TB) and silicosis among ex-miners from the South African gold mines has the potential to ease the backlog of lung examinations in clinical screening and medical adjudication for miners’ compensation. This study aimed to determine whether CAD systems developed to date primarily for TB were able to identify TB (without distinction between prior and active disease) and silicosis (or “other abnormality”) in this population. Methods: A total of 501 chest X-rays (CXRs) from a screening programme were submitted to two commercial CAD systems for detection of “any abnormality”, TB (any) and silicosis. The outcomes were tested against the readings of occupational medicine specialists with experience in reading miners’ CXRs. Accuracy of CAD against the readers was calculated as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Sensitivity and specificity were derived using a threshold requiring at least 90% sensitivity. Results: One system was able to detect silicosis and/or TB with high AUCs (>0.85) against both readers, and specificity > 70% in most of the comparisons. The other system was able to detect “any abnormality” and TB with high AUCs, but with specificity < 70%. Conclusion: CAD systems have the potential to come close to expert readers in the identification of TB and silicosis in this population. The findings underscore the need for CAD systems to be developed and validated in specific use-case settings.
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spelling pubmed-95650352022-10-15 Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines Ehrlich, Rodney Barker, Stephen te Water Naude, Jim Rees, David Kistnasamy, Barry Naidoo, Julian Yassi, Annalee Int J Environ Res Public Health Article Background: Computer-aided detection (CAD) of pulmonary tuberculosis (TB) and silicosis among ex-miners from the South African gold mines has the potential to ease the backlog of lung examinations in clinical screening and medical adjudication for miners’ compensation. This study aimed to determine whether CAD systems developed to date primarily for TB were able to identify TB (without distinction between prior and active disease) and silicosis (or “other abnormality”) in this population. Methods: A total of 501 chest X-rays (CXRs) from a screening programme were submitted to two commercial CAD systems for detection of “any abnormality”, TB (any) and silicosis. The outcomes were tested against the readings of occupational medicine specialists with experience in reading miners’ CXRs. Accuracy of CAD against the readers was calculated as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Sensitivity and specificity were derived using a threshold requiring at least 90% sensitivity. Results: One system was able to detect silicosis and/or TB with high AUCs (>0.85) against both readers, and specificity > 70% in most of the comparisons. The other system was able to detect “any abnormality” and TB with high AUCs, but with specificity < 70%. Conclusion: CAD systems have the potential to come close to expert readers in the identification of TB and silicosis in this population. The findings underscore the need for CAD systems to be developed and validated in specific use-case settings. MDPI 2022-09-29 /pmc/articles/PMC9565035/ /pubmed/36231700 http://dx.doi.org/10.3390/ijerph191912402 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ehrlich, Rodney
Barker, Stephen
te Water Naude, Jim
Rees, David
Kistnasamy, Barry
Naidoo, Julian
Yassi, Annalee
Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines
title Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines
title_full Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines
title_fullStr Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines
title_full_unstemmed Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines
title_short Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines
title_sort accuracy of computer-aided detection of occupational lung disease: silicosis and pulmonary tuberculosis in ex-miners from the south african gold mines
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565035/
https://www.ncbi.nlm.nih.gov/pubmed/36231700
http://dx.doi.org/10.3390/ijerph191912402
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