COMPARATIVE EVALUATION OF THE DIAGNOSTIC ACCURACY OF ARTIFICIAL INTELLIGENCE-ASSISTED TOOLS AND CONVENTIONAL TOOLS FOR PULMONARY TUBERCULOSIS SCREENING: A SYSTEMATIC REVIEW AND META-ANALYSIS (Proceedings of the Global Public Health Conference, 2025)
Tuberculosis is a preventable and curable disease, however, it remains as the second leading cause of death worldwide. Systematic screening for TB is one of the key active approaches of the End TB Strategy. However, conventional tools for TB screening have some limitations. AI-based algorithms could be developed, which can help in improving the performance of conventional screening methods. This study intends to evaluate the diagnostic test accuracy of AI-assisted PTB screening tools by meta-analyzing existing literature, and comparison of sensitivity and specificity, as well as assessment of factors which may influence the diagnostic performance of AI-assisted TB screening tools. Literature search was done through electronic databases, reference tracking, and library search. The Preferred Reporting of Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram was used to report the selection and screening of relevant studies between 2014 to 2024. A final count of 31 studies were included in this analysis. Quality assessment was done through the use of QUADAS-C tool. Meta analysis was done through RevMan 5.4.1 and STATA 17. Sensitivity and specificity were used in this analysis. A subgroup analysis was also conducted. The AI-assisted screening tools for pulmonary tuberculosis showed a pooled sensitivity of 93.84% (95% CI: 90.88-95.88) and 83.27% (95% CI: 73.41-89.97), and a diagnostic odds ratio (DOR) of 75.829 (95% CI: 33.19-173.23). Machine Learning (ML) algorithms yielded the highest sensitivity and specificity at 95.06% (95% CI:83.57-98.64) and 91.01% (95% CI: 76.76-96.88) respectively among the AI algorithm subgroup. The results of the meta-analysis done show that AI-assisted screening tools for pulmonary tuberculosis are viable options to improve screening for pulmonary tuberculosis. More robust, multi-center clinical studies regarding the diagnostic accuracy of these AI-assisted tools must be conducted in order to ensure a more valid and generalizable study.
Keywords: diagnostic accuracy; screening; meta-analysis; tuberculosis