Beyond accuracy: evaluating the operational feasibility and diagnostic yield of CAD4TB vs. Timika score for scalable TB screening in low-resource settings

Artificial intelligence has shown promise in enhancing tuberculosis care, but its use in resource-limited settings like Indonesia remains underexplored. This cross-sectional retrospective single-centre study evaluates the diagnostic performance of CAD4TB in screening Indonesian patients suspected of tuberculosis using chest x-ray (CXR) images, comparing its efficacy to the Timika score assessed by experts. We analyzed CXR images from 3,254 patients (2018–2020), including 600 with microbiological confirmation, of whom 46 had smear-positive pulmonary tuberculosis (PTB). CAD4TB demonstrated an area under the curve (AUC) of 0.778 (95% CI 0.712–0.844) when compared to acid-fast bacilli (AFB) results without a time interval. With a ≤7-day interval between CXR and AFB data, CAD4TB showed an AUC of 0.767 (95% CI 0.668–0.866), comparable to the Timika score of 0.726 (95% CI 0.632–0.820). Additionally, CAD4TB exhibited superior specificity (71.43% vs. 57.64%, p 0.001) while maintaining a fixed sensitivity of 73.91%. These findings suggest that CAD4TB outperforms the Timika score and holds promise as a rapid tuberculosis screening tool in resource-limited settings like Indonesia.