The Future of Mycobacterium Tuberculosis Diagnostics: Integrating Mass Spectrometry, AI, and Genomics

Abstract

Background: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health threat, with significant morbidity and mortality exacerbated by the emergence of multidrug-resistant strains and diagnostic challenges in high-burden settings. Objective: This review aimed to critically evaluate recent advancements in TB diagnostics, focusing on the integration of mass spectrometry, artificial intelligence, and genomics in comparison to conventional and molecular methods, and to assess their clinical applicability and limitations. Methods: A narrative review was conducted by systematically searching PubMed, Scopus, Web of Science, and Google Scholar for English-language peer-reviewed articles published from 2010 to 2023, using structured inclusion and exclusion criteria. Diagnostic performance, comparative accuracy, operational feasibility, and implementation barriers were extracted and synthesized across diverse populations. Results: Molecular diagnostics such as Xpert MTB/RIF have substantially improved rapid detection and rifampicin resistance screening, with pooled sensitivities above 90%, while advanced modalities including next-generation sequencing and mass spectrometry offer comprehensive resistance profiling but remain limited by cost and infrastructure requirements. Immunological assays provide high specificity for latent infection but cannot reliably distinguish active disease. Evidence synthesis reveals persistent gaps in pediatric, extrapulmonary, and HIV-positive populations, and underscores the need for large-scale validation and equitable deployment. Conclusion: Advanced TB diagnostics offer significant promise but must be strategically integrated with established approaches and rigorously validated in real-world settings to enhance global TB control and patient outcomes.