A Systematic Review and Meta-analysis on Innovative Approaches in Tuberculosis Diagnosis: Challenges and Future...

Tuberculosis (TB) has continued to be one of the global threats, affecting millions of individuals globally, including the maternal and child health (MCH) populations and individuals with HIV/AIDS. TB infected 10.8 million individuals, leading to 1.25 million deaths across the globe annually, leaving 4 million missing cases contributing to the global TB burden. This study aims to unveil innovative approaches to TB diagnosis, challenges, and the future direction of this field.

A comprehensive literature search was conducted to retrieve studies published between 2019 and 2024 from PubMed, Google Scholar, and Web of Science. The studies were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and screened using the Rayyan tool. The quality and risk of bias of the included studies were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 for diagnostic accuracy studies and Strengthening the Reporting of Observational Studies in Epidemiology for observational studies. A random effects model was employed to calculate the pooled sensitivity and specificity, while Egger's test and funnel plots were utilized to evaluate publication bias. R and MetaDTA software were used for all the statistical analyses.

The review included 25 studies with sample sizes ranging from 100 to 6,520 participants and assessed various innovative approaches for diagnosing TB, including molecular methods, biomarker-based techniques, and artificial intelligence (AI) applications. The most common approach was molecular testing, specifically cartridge-based tests. The overall sensitivity of these methods was 0.79 (95% confidence interval [CI]: 0.70-0.86), with a heterogeneity index (I²) of 92.9% and a p < 0.0001. The specificity was recorded at 0.93 (95% CI: 0.87-0.96), with an I² of 94.8% and a p < 0.0001.

This review reinforces the promise of innovative diagnostic methods, especially cartridge-based molecular tests, for improving TB detection. However, moderate sensitivity and high heterogeneity emphasize the need for cautious implementation and further validation of new tools such as the AI-based and biomarker-based. Strategic investment in research and contextual deployment is critical for closing the TB diagnosis gap globally.