  Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO
recommends chest radiographs (CXRs) for TB screening, the limited availability
of CXR interpretation is a barrier. We trained a deep learning system (DLS) to
detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and
Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy
student semi-supervised learning. Evaluation was on (1) a combined test set
spanning China, India, US, and Zambia, and (2) an independent mining population
in South Africa. Given WHO targets of 90% sensitivity and 70% specificity, the
DLS's operating point was prespecified to favor sensitivity over specificity.
On the combined test set, the DLS's ROC curve was above all 9 India-based
radiologists, with an AUC of 0.90 (95%CI 0.87-0.92). The DLS's sensitivity
(88%) was higher than the India-based radiologists (75% mean sensitivity),
p<0.001 for superiority; and its specificity (79%) was non-inferior to the
radiologists (84% mean specificity), p=0.004. Similar trends were observed
within HIV positive and sputum smear positive sub-groups, and in the South
Africa test set. We found that 5 US-based radiologists (where TB isn't endemic)
were more sensitive and less specific than the India-based radiologists (where
TB is endemic). The DLS also remained non-inferior to the US-based
radiologists. In simulations, using the DLS as a prioritization tool for
confirmatory testing reduced the cost per positive case detected by 40-80%
compared to using confirmatory testing alone. To conclude, our DLS generalized
to 5 countries, and merits prospective evaluation to assist cost-effective
screening efforts in radiologist-limited settings. Operating point flexibility
may permit customization of the DLS to account for site-specific factors such
as TB prevalence, demographics, clinical resources, and customary practice
patterns.
