Cost-effectiveness analysis of computer aided detection for tuberculosis in Ghana: A decision-analytic modeling study

Background: The financial sustainability of universal molecular testing for tuberculosis (TB) remains a critical challenge in high-burden settings. In Ghana, the national program utilizes GeneXpert MTB/RIF for confirmation, yet the high unit cost necessitates efficient triage tools. This study evaluates the cost-effectiveness of Computer-Aided Detection (CAD) based triage compared to the standard of care in Ghana. Methods: This is a prospective cross-sectional diagnostic accuracy study, with an embedded decision-analytic economic evaluation, using primary data from 701 symptomatic outpatients across 23 facilities in Ghana. The cost-effectiveness analysis compared the vendor-recommended threshold of 51 with locally calibrated CAD scores of 56 (weighted Youden) and 68 (standard Youden). These were evaluated against the current Standard of Care (symptom screening and human-read chest X-ray). Health outcomes were measured in Quality-Adjusted Life Years (QALYs) lost, applying a severe morbidity penalty to missed TB cases (False Negatives) to account for prolonged suffering. The primary outcome was the Incremental Cost-Effectiveness Ratio (ICER). Analysis was conducted with R version 4.3. Results: The CAD-based triage at the weighted Youden threshold of 56 was identified as the optimal clinical strategy, being both more effective and less expensive than the current Standard of Care (SoC). The total health system pathway cost for the CAD-56 strategy and SoC was US$23,449.01 and US$33,239.09, respectively. Threshold of CAD-56 instead of the SoC saved the health system US$9,790.08 on 701 individuals screened and yielding an incremental health gain of 10.08 Quality-Adjusted Life Years (QALYs). Conclusion: Implementing locally calibrated CAD4TB version 7 at an optimized threshold of 56 as a gatekeeper for molecular testing would be a dominant economic strategy for TB control in Ghana. The findings provide a preliminary evidence for further assessment with nationwide data before national rollout of AI-based triage.