Advancements in tuberculosis diagnostics: An update

Area of Science:

• Infectious Diseases
• Medical Diagnostics
• Microbiology

Background:

• Tuberculosis (TB) remains a significant global health threat, exacerbated by rising drug resistance and treatment failures.
• Conventional TB diagnostic methods like microscopy and chest X-rays have limitations in sensitivity, accuracy, and speed.
• The increasing incidence of TB, with 8.2 million new cases reported in 2023, necessitates improved diagnostic strategies.

Purpose of the Study:

• To review and highlight modern diagnostic tools for tuberculosis (TB).
• To assess the potential of novel techniques in characterizing Mycobacterium tuberculosis (Mtb) and detecting drug resistance.
• To emphasize the importance of point-of-care (POC) diagnostics for effective TB management in resource-limited settings.

Main Methods:

• Review of conventional diagnostic techniques for TB detection.
• Exploration of advanced biochemical, molecular, and immunological diagnostic tools.
• Focus on emerging technologies such as T-SPOT, artificial intelligence (AI), electronic nose, RT PCR, TB LAM, CRISPR, and biosensor-based detection.

Main Results:

• Modern diagnostic tools offer enhanced sensitivity, specificity, and speed compared to conventional methods.
• Novel techniques can identify Mtb strains and detect mutations associated with drug resistance.
• Point-of-care (POC) diagnostics show promise for rapid TB diagnosis in diverse healthcare settings.

Conclusions:

• Advanced diagnostic tools are crucial for overcoming limitations of traditional TB detection methods.
• The integration of technologies like AI and CRISPR can significantly improve TB diagnosis and patient management.
• Developing accessible and accurate POC diagnostic tools is essential for controlling the global TB epidemic.