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.
Worodria et al. (Fri,) studied this question.