A multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for tuberculosis
Annals of the American Thoracic Society, 2026
Abstract
Rationale: Computer-aided detection algorithms for automated chest X-ray reading have been endorsed by the World Health Organization for tuberculosis triage, but independent, multi-country assessment of current products is needed to guide implementation.
Objective: We included chest X-rays from adults who presented to outpatient facilities with at least two weeks of cough in India, Madagascar, the Philippines, South Africa, Tanzania, Uganda, and Vietnam.
Methods: We calculated and compared the accuracy overall, by country and key groups for seven computer-aided detection algorithms: CAD4TB, qXR, INSIGHT CXR, DrAid, Genki, InferRead, and Radify. We determined if any computer-aided detection product could achieve the minimum target accuracy for a tuberculosis triage test (≥90% sensitivity and ≥70% specificity).
Measurements and Main Results: Of 3,901 individuals included, the median age was 41 years (IQR 29-54), 12.9% were people living with HIV, 8.2% living with diabetes, and 21.2% had a prior history of tuberculosis. Specificity ranged from 30.9-73.5% at 90% sensitivity. CAD4TB achieved the highest specificity at 90% sensitivity (73.5% specific, 95% CI 71.9-75.1), although qXR and INSIGHT CXR also achieved the target 70% specificity. There was heterogeneity by country and subgroup that improved with population-specific thresholds, except for people living with HIV, 50 years and older or with a history of tuberculosis.
Conclusions: Multiple computer-aided detection algorithms achieved the minimum target accuracy for a tuberculosis triage test among symptomatic individuals with cough. Further efforts are needed to integrate computer-aided detection into routine tuberculosis case detection programs in high-burden communities.
