Deep learning based detection of active pulmonary tuberculosis from chest radiographs in a retrospective prospective multicenter study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, The First People's Hospital of Kashi Prefecture, Kashi, China.
- Xinjiang Key Laboratory of Artificial Intelligence Assisted Imaging Diagnosis, Kashi, China.
- Xinjiang Academy of Medical Sciences, Department of Scientific Research, Urumqi, China.
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China.
- Department of Radiology, The Fourth Affiliated Hospital of Xinjiang Medical University (Xinjiang Hospital of Traditional Chinese Medicine), Urumqi, Xinjiang, China.
- Clinical Medical Research Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, China.
Abstract
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.