Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high burden countries

Abstract Background In 2022, fewer than half of persons with tuberculosis (TB) have access to molecular diagnostic tests for TB due to their high costs. Studies have found that computer-aided detection using artificial intelligence (AI) for chest X-ray (CXR) and sputum specimen pooling can each reduce testing costs. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving computer-aided CXR to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing as well as the theoretical expansion in diagnostic coverage. Results In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and guide pooled and individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34–160% across the different approaches and countries. Conclusions Using a combination of AI and CXR to inform different pooling strategies may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.