Abstract :
Tuberculosis (TB) continues to pose a serious threat to world health, improvements in diagnostic technologies are essential for both early identification and successful treatment. The sensitivity, rapidity, and accessibility of traditional diagnostic procedures, such as sputum smear microscopy and culture methods, are limited. Accuracy, turnaround time, and scalability have all increased with recent advancements in TB tests. Mycobacterium tuberculosis and medication resistance can be quickly identified thanks to molecular diagnostics like GeneXpert MTB/RIF and Truenat. TB strains and resistance mutations can be accurately identified using next-generation sequencing (NGS) and CRISPR-based diagnostics. Early screening is improved by artificial intelligence (AI)-assisted imaging methods, such as automated chest X-ray interpretation, especially in environments with limited resources. The sensitivity and specificity of point-of-care diagnostics are enhanced by biosensors and nanotechnology-based methods. Latent TB infection diagnosis is also made easier by immunodiagnostic procedures such host biomarker-based tests and interferon-gamma release assays (IGRAs). Personalized TB diagnostics may be developed by the use of multi-omics techniques that combine transcriptomics, proteomics, and metabolomics. Additionally, new wearable and non-invasive breath-based detection techniques are becoming more popular. Cost, accessibility, and integration into healthcare systems are still issues in spite of these developments. Future studies should concentrate on integrating digital health to improve real-time surveillance, scalability, and affordability. By guaranteeing prompt and precise detection, the ongoing development of TB diagnostics will be essential to reaching the worldwide TB elimination targets.
Keyword :
Tuberculosis, Molecular diagnostics, Artificial intelligence, Biosensors, Next-generation sequencing.