Deep learning based detection of active pulmonary tuberculosis from chest radiographs in a retrospective prospective multicenter study

Active tuberculosis remains a major global health problem, and distinguishing active from non-active disease on chest radiographs could accelerate triage where radiology resources are limited. We developed ShuffleNetV2 using 4,613 radiographs from two centers and evaluated it in retrospective external (1,318 patients) and prospective (560 patients) cohorts. The model achieved AUCs of 0.917 and 0.896, respectively, and performance was stable across prespecified demographic and scanner subgroups. In the prospective cohort, artificial-intelligence assistance increased radiologist AUCs to 0.897-0.937 across experience levels. These findings support ShuffleNetV2 as a rapid decision-support tool for prioritizing confirmatory clinical and microbiological assessment; positive predictions should not be used alone to initiate treatment.