Development and Validation of a Deep Learning-Based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.

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Abstract

Detection of active pulmonary tuberculosis (TB) on chest radiographs (CR) is critical for the diagnosis and screening of TB. An automated system may help streamline the TB screening process and improve diagnostic performance.
We developed a deep-learning-based automatic detection (DLAD) algorithm, using 54,221 normal CRs and 6,768 CRs with active pulmonary TB, which were labeled and annotated by 13 board-certified radiologists. The performance of DLAD was validated using six external multi-center, multi-national datasets. To compare the performances of DLAD with physicians, an observer performance test was conducted by 15 physicians including non-radiology physicians, board-certified radiologists, and thoracic radiologists. Image-wise classification and lesion-wise localization performances were measured using area under the receiver operating characteristic (ROC) curves, and area under the alternative free-response ROC curves, respectively. Sensitivities and specificities of DLAD were calculated using two cutoffs [high sensitivity (98%) and high specificity (98%)] obtained through in-house validation.
DLAD demonstrated classification performances of 0.977-1.000 and localization performance of 0.973-1.000. Sensitivities and specificities for classification were 94.3-100% and 91.1-100% using the high sensitivity cutoff and 84.1-99.0% and 99.1-100% using the high specificity cutoff. DLAD showed significantly higher performance in both classification (0.993 vs. 0.746-0.971) and localization (0.993 vs. 0.664-0.925) compared to all groups of physicians.
Our DLAD demonstrated excellent and consistent performance in the detection of active pulmonary TB on CR, outperforming physicians including thoracic radiologists.