Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease

Faiz Ahmad Khan; Arman Majidulla; Gamuchirai Tavaziva; Ahsana Nazish; Syed Kumail Abidi; Andrea Benedetti; Dick Menzies; James C Johnston; Aamir Javed Khan; Saima Saeed

doi:10.1016/S2589-7500(20)30221-1

Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease

Lancet Digit Health. 2020 Nov;2(11):e573-e581. doi: 10.1016/S2589-7500(20)30221-1. Epub 2020 Oct 19.

Authors

Affiliations

¹ McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine and Department of Epidemiology, McGill University, Montreal, Canada. Electronic address: faiz.ahmadkhan@mcgill.ca.
² Interactive Research and Development Pakistan, Karachi, Pakistan.
³ McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
⁴ The Indus Hospital, Karachi, Pakistan.
⁵ McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada.
⁶ Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine and Department of Epidemiology, McGill University, Montreal, Canada.
⁷ McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine and Department of Epidemiology, McGill University, Montreal, Canada.
⁸ Ghori TB Clinic, University of British Columbia, Vancouver, BC, Canada.
⁹ Interactive Research and Development Global, Singapore.
¹⁰ Global Health Directorate, Indus Health Network, Karachi, Pakistan.

PMID: 33328086
DOI: 10.1016/S2589-7500(20)30221-1

Abstract

Background: Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis software meet WHO recommendations for minimal sensitivity and specificity as pulmonary tuberculosis triage tests.

Methods: We recruited symptomatic adults at the Indus Hospital, Karachi, Pakistan. We compared two software, qXR version 2.0 (qXRv2) and CAD4TB version 6.0 (CAD4TBv6), with a reference of mycobacterial culture of two sputa. We assessed qXRv2 using its manufacturer prespecified threshold score for chest x-ray classification as tuberculosis present versus not present. For CAD4TBv6, we used a data-derived threshold, because it does not have a prespecified one. We tested for non-inferiority to preset WHO recommendations (0·90 for sensitivity, 0·70 for specificity) using a non-inferiority limit of 0·05. We identified factors associated with accuracy by stratification and logistic regression.

Findings: We included 2198 (92·7%) of 2370 enrolled participants. 2187 (99·5%) of 2198 were HIV-negative, and 272 (12·4%) had culture-confirmed pulmonary tuberculosis. For both software, accuracy was non-inferior to WHO-recommended minimum values (qXRv2 sensitivity 0·93 [95% CI 0·89-0·95], non-inferiority p=0·0002; CAD4TBv6 sensitivity 0·93 [0·90-0·96], p<0·0001; qXRv2 specificity 0·75 [0·73-0·77], p<0·0001; CAD4TBv6 specificity 0·69 [0·67-0·71], p=0·0003). Sensitivity was lower in smear-negative pulmonary tuberculosis for both software, and in women for CAD4TBv6. Specificity was lower in men and in those with previous tuberculosis, and reduced with increasing age and decreasing body mass index. Smoking and diabetes did not affect accuracy.

Interpretation: In an HIV-negative population, these software met WHO-recommended minimal accuracy for pulmonary tuberculosis triage tests. Sensitivity will be lower when smear-negative pulmonary tuberculosis is more prevalent.

Funding: Canadian Institutes of Health Research.

Publication types

Evaluation Study
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Age Factors
Body Mass Index
Deep Learning*
Female
HIV Infections
Humans
Image Processing, Computer-Assisted / methods*
Lung / diagnostic imaging
Lung / microbiology
Lung / pathology*
Male
Middle Aged
Mycobacterium tuberculosis / growth & development
Pakistan
Prospective Studies
Radiology / methods*
Sensitivity and Specificity
Sex Factors
Software*
Sputum / microbiology
Triage*
Tuberculosis, Pulmonary / diagnosis*
Tuberculosis, Pulmonary / diagnostic imaging
Tuberculosis, Pulmonary / microbiology
X-Rays
Young Adult