ARTICLE: Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study
AUTHORS: Joowhan Sung, Peter James Kitonsa, Annet Nalutaaya, David Isooba, Susan Birabwa, Keneth Ndyabayunga, Rogers Okura, Jonathan Magezi, Deborah Nantale, Ivan Mugabi, Violet Nakiiza, David W Dowdy, Achilles Katamba, Emily A Kendall
JOURNAL: Lancet Digit Health. 2025 Dec;7(12):100934. doi: 10.1016/j.landig.2025.100934. Epub 2025 Nov 26.
Abstract
Background: Computer-aided detection (CAD) software analyses chest x-rays for features suggestive of tuberculosis and provides a numeric abnormality score. However, estimates of CAD accuracy for tuberculosis screening are hindered by the scarcity of confirmatory data among people with lower x-ray scores, including those without symptoms. Additionally, the appropriate x-ray score thresholds for obtaining further testing might vary according to population and client characteristics. We aimed to evaluate the accuracy of CAD among all screened individuals and assess whether stratifying CAD thresholds by age and sex could improve performance.
Methods: In this cross-sectional, diagnostic accuracy study, we screened for tuberculosis in individuals aged 15 years and older in Uganda using portable chest x-rays with CAD (qXR version 3.2). Participants not on active tuberculosis treatment were offered screening regardless of their symptoms. We included data from all participants from both facility-based and community-based sites who were screened from June 1, 2022 (study start), to March 31, 2024. Individuals with x-ray scores above a threshold of 0·1 (range 0-1) were asked to provide sputum for Xpert MTB/RIF Ultra (Xpert) testing. We estimated the diagnostic accuracy (sensitivity, specificity, and area under the curve [AUC]) of CAD for detecting Xpert-positive tuberculosis when using the same threshold for all individuals (under different assumptions about tuberculosis prevalence among people with x-ray scores <0·1), and compared this estimate with approaches stratified by age, sex, or both.
Findings: 54 840 individuals were assessed for eligibility, 52 835 of whom were screened for tuberculosis using CAD. The median age was 38 years (IQR 26-50), 23 586 (44·6%) participants were male, and 29 249 (55·4%) were female. 8949 (16·9%) had x-ray scores of 0·1 or more. Of 7219 participants with valid Xpert results, 382 (5·3%) were Xpert-positive, including 81 with trace results. Assuming 0·1% of participants with x-ray scores less than 0·1 would have been Xpert-positive if tested, qXR had an estimated AUC of 0·92 (95% CI 0·90-0·94) for Xpert-positive tuberculosis. Stratifying x-ray score thresholds according to age and sex improved accuracy; for example, at 96·1% (95% CI 95·9-96·3) specificity, estimated sensitivity was 75·0% (69·9-79·5) for a universal threshold (of ≥0·65) versus 76·9% (71·9-81·2) for thresholds stratified by age and sex (p=0·046).
Interpretation: Our findings suggest that the accuracy of CAD for tuberculosis screening among all screening participants, including those without symptoms or abnormal chest x-rays, is higher than previously estimated. Stratifying x-ray score thresholds based on client characteristics such as age and sex could further improve accuracy, enabling a more effective and personalised approach to tuberculosis screening.
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