Performance of Universal and Stratified Computer-Aided Detection Thresholds for Chest X-Ray-Based Tuberculosis...

Computer-aided detection (CAD) software analyzes chest X-rays for features suggestive of tuberculosis (TB) and provides a numeric abnormality score. However, estimates of CAD accuracy for TB screening are hindered by the lack 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 may vary according to population and client characteristics.

We screened for TB in Ugandan individuals aged ≥15 years using portable chest X-rays with CAD (qXR v3). Participants were offered screening regardless of their symptoms. Those with X-ray scores above a threshold of 0.1 (range, 0 - 1) were asked to provide sputum for Xpert Ultra testing. We estimated the diagnostic accuracy of CAD for detecting Xpert-positive TB when using the same threshold for all individuals (under different assumptions about TB prevalence among people with X-ray scores <0.1), and compared this estimate to age- and/or sex-stratified approaches.

Of 52,835 participants screened for TB using CAD, 8,949 (16.9%) had X-ray scores ≥0.1. Of 7,219 participants with valid Xpert Ultra results, 382 (5.3%) were Xpert-positive, including 81 with trace results. Assuming 0.1% of participants with X-ray scores <0.1 would have been Xpert-positive if tested, qXR had an estimated AUC of 0.92 (95% confidence interval 0.90-0.94) for Xpert-positive TB. Stratifying X-ray score thresholds according to age and sex improved accuracy; for example, at 96.1% specificity, estimated sensitivity was 75.0% for a universal threshold (of ≥0.65) versus 76.9% for thresholds stratified by age and sex (p=0.046).

The accuracy of CAD for TB 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 personalized approach to TB screening.

National Institutes of Health.