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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

Author

Listed:
  • Rodney Ehrlich

    (Division of Occupational Medicine, School of Public Health and Family Medicine, University of Cape Town, Cape Town 7925, South Africa)

  • Stephen Barker

    (School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada)

  • Jim te Water Naude

    (Division of Occupational Medicine, School of Public Health and Family Medicine, University of Cape Town, Cape Town 7925, South Africa
    Diagnostic Medicine, Cape Town 7708, South Africa)

  • David Rees

    (School of Public Health, University of the Witwatersrand, Johannesburg 2193, South Africa)

  • Barry Kistnasamy

    (Office of the Compensation Commissioner for Occupational Diseases, Johannesburg 2001, South Africa)

  • Julian Naidoo

    (Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa)

  • Annalee Yassi

    (School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada)

Abstract

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.

Suggested Citation

  • Rodney Ehrlich & Stephen Barker & Jim te Water Naude & David Rees & Barry Kistnasamy & Julian Naidoo & Annalee Yassi, 2022. "Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines," IJERPH, MDPI, vol. 19(19), pages 1-14, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12402-:d:928851
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    References listed on IDEAS

    as
    1. Miriam Harris & Amy Qi & Luke Jeagal & Nazi Torabi & Dick Menzies & Alexei Korobitsyn & Madhukar Pai & Ruvandhi R Nathavitharana & Faiz Ahmad Khan, 2019. "A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-19, September.
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