Integrating AI with PCR for Tuberculosis Diagnosis: Evaluating a Deep Learning Model for Chest X-Rays

Tuberculosis (TB) remains a major global health challenge, and early, accurate diagnosis is essential for effective disease control. Chest radiography (CXR) is widely used for TB screening because of its accessibility, yet its limited specificity necessitates confirmatory molecular testing such as polymerase chain reaction (PCR) assays. This study aimed to evaluate the diagnostic performance of a deep learning model (DLM) for TB detection using CXR and to compare its predictive accuracy with PCR results, specifically in a low-burden region. A retrospective dataset of CXR images and corresponding PCR findings was obtained from two hospitals. The DLM, based on the CheXzero vision transformer, was trained on a large imaging dataset and evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics. Internal and external validation sets assessed sensitivity, specificity, and predictive values, with subgroup analyses according to imaging modality, demographics, and comorbidities. The model achieved an AUC of 0.915 internally and 0.850 externally, maintaining good sensitivity and specificity, though performance declined when limited to PCR-confirmed cases. Accuracy was lower for older adults and those with chronic kidney disease, chronic obstructive pulmonary disease, or heart failure. These findings suggest AI-assisted CXR screening may support TB detection in resource-limited settings, but PCR confirmation remains essential.