Implementation of Digital Chest X-Ray with Computer-Aided Detection for Tuberculosis Screening Among Persons with Advanced HIV Disease in Maputo, Mozambique: A Retrospective Cohort Analysis

Background: Tuberculosis (TB) remains the leading cause of death among persons with advanced HIV disease (AHD) in high HIV-burden settings. Digital chest X-ray (dCXR) with computer-aided detection (CAD) is a promising tool to overcome human resource constraints and improve TB case detection. This study evaluates the real-world implementation and performance of dCXR/CAD for TB screening within a specialized AHD clinic in Maputo, Mozambique. Methods: We conducted a retrospective cohort analysis of 487 new AHD patients at Centro de Referência do Alto Maé (CRAM) from October 2023 to September 2024. Of these, 238 underwent dCXR with CAD interpretation. All patients underwent systematic TB screening according to Ministry of Health (MoH) guidelines. Using the recorded diagnosis of TB (bacteriologically confirmed or clinically diagnosed) as the reference standard, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the nationally adopted CAD threshold (≥0.5). Results: Among 238 AHD patients screened with CAD, 116 (49%) were diagnosed with TB. At the ≥0.5 threshold, sensitivity was 50% (58/116; 95% CI: 41–59), specificity 92% (112/122; 95% CI: 85–96), PPV 85% (58/68; 95% CI: 75–92), and NPV 65.9% (112/170; 95% CI: 58–73). TB diagnosis rates increased sharply with CAD score: 30% (43/143) in normal, 52% (15/27) in abnormal non-suggestive, and 85% (58/68) in suggestive cases. Bacteriological confirmation was low across all groups (19–26%), reflecting reliance on clinical diagnosis. Conclusions: Integrating dCXR/CAD into AHD care is feasible and identifies a high TB burden. However, at the adopted threshold of ≥0.5, CAD demonstrated high specificity but low sensitivity (50%) in this population, missing half of all TB cases. These findings suggest that CAD functions better as a confirmatory decision-support tool than a standalone screening test in AHD. Threshold optimization for this specific population warrants prospective evaluation. Implementation challenges including fragmented systems and lack of dedicated human resources must be addressed to realize CAD’s full potential in TB programs.