Abstract
Tuberculosis remains a major global health concern, particularly in high-burden countries where early detection is essential but often limited by insufficient radiological expertise. This study evaluated the diagnostic performance of a computer-aided detection system, CAD4TB, in interpreting chest X-ray images of suspected tuberculosis cases in a hospital setting in Indonesia. Using a retrospective, cross-sectional design, we analyzed chest radiographs from over 1,100 adult patients drawn from the national tuberculosis database. Images were processed using the CAD4TB system and independently reviewed by two experienced radiologists. Bacteriological test results were used as the diagnostic reference standard. At a CAD4TB index cutoff of 60, the tool achieved a sensitivity of 81.04% and a specificity of 63.80%. In comparison, radiologist interpretations achieved a sensitivity of 88.20% and a specificity of 58.18%. Subgroup analyses revealed improved diagnostic performance in individuals without pleural effusion, with CAD4TB sensitivity rising to 83.65% and radiologist sensitivity to 90.35%. CAD4TB also showed consistent specificity advantages across clinical subgroups, including those with prior tuberculosis history and HIV-negative status. These findings support the potential role of CAD4TB in assisting radiologists within hospital settings, especially in high-burden areas. Its time-efficiency and ease of use make it a valuable tool for integration into tuberculosis triage systems, particularly where patient complexity varies and access to expert readers is limited.