Recent developments in diagnosis of <i>Mycobacterium tuberculosis</i> : an overview

Tuberculosis (TB) is a highly infectious disease caused by <i>Mycobacterium tuberculosis</i>. The World Health Organization (WHO) has reported more than 8 million new or relapse TB cases and 1.25 million deaths in 2023. Even though the global End TB strategy implemented by WHO has reduced the TB incidence rate between the years 2015 and 2023 up to 8.3%, TB remains one of the leading infectious causes of death worldwide. Active tuberculosis can affect any part of the body, and the clinical symptoms include fever, weight loss, and night sweats, whereas latent TB does not show any clinical symptoms. One of the significant concerns regarding the control of TB is the reservoir nature of latent tuberculosis. The Bacillus Calmette-Guerin (BCG) vaccine helps control TB and is still used globally to combat it. Currently, two methods are primarily used to identify tuberculosis infection: the Tuberculin Skin Test (TST) and the Interferon-Gamma Release Assay (IGRA). A significant disadvantage of these two methods is the inability to differentiate between latent and active tuberculosis. Accurate and timely diagnosis of TB is essential for effective management, mainly due to the emergence of multidrug-resistant strains. Specific biomarkers are required to evaluate tuberculosis infection, and novel biomarkers are needed to develop new diagnostic methods for tuberculosis. This article presents a brief review of recent developments in TB diagnostics, covering immunological, molecular, and monoclonal antibody-based platforms. The review also discusses the emerging role of artificial intelligence and deep machine learning platforms as complementary diagnostic tools.