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Current trends in tuberculosis diagnosis: Advancements and challenges
International Journal of Allied Medical Sciences and Clinical Research,
Vol. 13 No. 2 (2025): 2025 Volume -13 - Issue 2
Abstract
Tuberculosis [TB] is still a worldwide health issue, and early and accurate diagnosis of TB is essential for the management of the disease. Although conventional diagnostic methods, including sputum smear microscopy and culture methods, provide some diagnostic information, these methods have challenges in terms of sensitivity, specificity, and turnaround time. Recent TB diagnostics include molecular-based diagnostics, such as GeneXpert MTB/RIF and line-probe assays, which provide rapid and highly sensitive TB detection, including in drug-resistant cases. Next-generation sequencing and whole genome sequencing [and other “omics” technology] show potential for isolating strains of Mycobacterium tuberculosis and identifying resistance typing and epidemiological surveillance. However, there remain challenges, including high cost, availability of resources in low-resource settings, and reliance on a skilled workforce. The role of artificial intelligence [AI] is being studied, particularly with respect to TB detection in imaging, to test the accuracy of AI programs and their ability to predict TB on chest radiographic interpretation. Future directions in TB diagnostics include further development of point-of-care diagnostics, reliance on AI- and machine learning-driven algorithms in imaging and data interpretation, and advancements in access to genomic sequencing and molecular diagnostics. Increased investment in research [and studies on the cost] and supportive policy frameworks for regulatory approval pathways and reducing costs will be important to ensure further implementation and uptake of diagnostics to achieve the goal of ending TB by 2035.
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- World Health Organization. Global Tuberculosis Report 2023. WHO Press; 2023.
- Horne DJ, Kohli M, Zifodya JS, et al. Xpert MTB/RIF and Xpert MTB/RIF Ultra for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev. 2019;6[6]:CD009593.
- Lawn SD, Nicol MP. Xpert® MTB/RIF assay: Development, evaluation, and implementation of a new rapid molecular diagnostic for tuberculosis and rifampicin resistance. Future Microbiol. 2011;6[9]:1067-1082.
- Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study. Lancet Infect Dis. 2015;15[10]:1193-1202.
- Pai M, Schito M. Tuberculosis diagnostics in 2020: Landscape, priorities, needs, and prospects. J Clin Microbiol. 2018;56[6]: e01506-17.
- Qin ZZ, Sander MS, Rai B, et al. Using artificial intelligence to improve chest X-ray interpretation for tuberculosis: A multi-site evaluation of the diagnostic accuracy of three deep-learning systems. Lancet Digit Health. 2019;1[5]:e343-e354.
- Parsons LM, Somoskövi Á, Gutierrez C, et al. Laboratory diagnosis of tuberculosis in resource-poor countries: Challenges and opportunities. Clin Microbiol Rev. 2011;24[2]:314-350.
- McNerney R, Daley P. Towards a point-of-care test for active tuberculosis: Obstacles and opportunities. Nat Rev Microbiol. 2011;9[3]:204-213.
- World Health Organization. WHO consolidated guidelines on tuberculosis: Module 3 – Diagnosis. WHO Press; 2021.
- Hillemann D, Rüsch-Gerdes S, Richter E. Evaluation of the GenoType® MTBDRplus assay for rapid detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis. J Clin Microbiol. 2007;45[7]:1935-1940.
- Walker TM, Merker M, Knoblauch AM, et al. A cluster-randomized trial of diagnostic whole-genome sequencing for multidrug-resistant tuberculosis. N Engl J Med. 2022;387[8]:703-714.
- Boehme CC, Nabeta P, Hillemann D, et al. Rapid molecular detection of tuberculosis and rifampin resistance. N Engl J Med. 2010;363[11]:1005-1015.
- Murphy K, Habib SS, Zaidi SMH, et al. Computer-aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci Rep. 2020;10[1]:5492.
- Meehan CJ, Goig GA, Kohl TA, et al. Whole genome sequencing of Mycobacterium tuberculosis: current applications and future opportunities. Nat Rev Microbiol. 2019;17[9]:533-545.
- Yager P, Domingo GJ, Gerdes J. Point-of-care diagnostics for global health. Annu Rev Biomed Eng. 2008; 10:107-144.
- Myhrvold C, Freije CA, Gootenberg JS, et al. Field-deployable viral diagnostics using CRISPR-Cas13. Science. 2018;360[6387]:444-448.
- Chin CD, Linder V, Sia SK. Commercialization of microfluidic point-of-care diagnostic devices. Lab Chip. 2012;12[12]:2118-2134.
- Rajchgot J, Thomas S, Kidd M, et al. Mobile phone–based sputum smear microscopy for tuberculosis diagnosis: A field evaluation in Uganda. Sci Rep. 2019;9[1]:11023.
- Albert H, Nathavitharana RR, Isaacs C, et al. Development, roll-out, and impact of Xpert MTB/RIF for tuberculosis: What lessons have we learned? Eur Respir J. 2016;48[2]:516-525.
- Marais BJ, Graham SM, Maeurer M, et al. Progress and challenges in childhood tuberculosis. Lancet Infect Dis. 2013;13[4]:287-289.
- Sun AY, den Boon S, Shah M, et al. Xpert MTB/RIF Ultra for tuberculosis testing in children: A systematic review and meta-analysis. Eur Respir J. 2022;60[4]:2103198.
- Dheda K, Gumbo T, Maartens G, et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017;5[4]:291-360.
- Broger T, Nicol MP, Szekely R, et al. Diagnostic accuracy of a novel lateral flow lipoarabinomannan assay for tuberculosis in people with HIV. Eur Respir J. 2020;55[2]:1902124.
- Xu P, Syaifullah A, Xu J. Artificial intelligence in tuberculosis diagnostics: A review of current progress and future prospects. Front Microbial. 2021; 12:661102.
- Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study. Lancet Infect Dis. 2015;15[10]:1193-1202.
References
World Health Organization. Global Tuberculosis Report 2023. WHO Press; 2023.
Horne DJ, Kohli M, Zifodya JS, et al. Xpert MTB/RIF and Xpert MTB/RIF Ultra for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev. 2019;6[6]:CD009593.
Lawn SD, Nicol MP. Xpert® MTB/RIF assay: Development, evaluation, and implementation of a new rapid molecular diagnostic for tuberculosis and rifampicin resistance. Future Microbiol. 2011;6[9]:1067-1082.
Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study. Lancet Infect Dis. 2015;15[10]:1193-1202.
Pai M, Schito M. Tuberculosis diagnostics in 2020: Landscape, priorities, needs, and prospects. J Clin Microbiol. 2018;56[6]: e01506-17.
Qin ZZ, Sander MS, Rai B, et al. Using artificial intelligence to improve chest X-ray interpretation for tuberculosis: A multi-site evaluation of the diagnostic accuracy of three deep-learning systems. Lancet Digit Health. 2019;1[5]:e343-e354.
Parsons LM, Somoskövi Á, Gutierrez C, et al. Laboratory diagnosis of tuberculosis in resource-poor countries: Challenges and opportunities. Clin Microbiol Rev. 2011;24[2]:314-350.
McNerney R, Daley P. Towards a point-of-care test for active tuberculosis: Obstacles and opportunities. Nat Rev Microbiol. 2011;9[3]:204-213.
World Health Organization. WHO consolidated guidelines on tuberculosis: Module 3 – Diagnosis. WHO Press; 2021.
Hillemann D, Rüsch-Gerdes S, Richter E. Evaluation of the GenoType® MTBDRplus assay for rapid detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis. J Clin Microbiol. 2007;45[7]:1935-1940.
Walker TM, Merker M, Knoblauch AM, et al. A cluster-randomized trial of diagnostic whole-genome sequencing for multidrug-resistant tuberculosis. N Engl J Med. 2022;387[8]:703-714.
Boehme CC, Nabeta P, Hillemann D, et al. Rapid molecular detection of tuberculosis and rifampin resistance. N Engl J Med. 2010;363[11]:1005-1015.
Murphy K, Habib SS, Zaidi SMH, et al. Computer-aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci Rep. 2020;10[1]:5492.
Meehan CJ, Goig GA, Kohl TA, et al. Whole genome sequencing of Mycobacterium tuberculosis: current applications and future opportunities. Nat Rev Microbiol. 2019;17[9]:533-545.
Yager P, Domingo GJ, Gerdes J. Point-of-care diagnostics for global health. Annu Rev Biomed Eng. 2008; 10:107-144.
Myhrvold C, Freije CA, Gootenberg JS, et al. Field-deployable viral diagnostics using CRISPR-Cas13. Science. 2018;360[6387]:444-448.
Chin CD, Linder V, Sia SK. Commercialization of microfluidic point-of-care diagnostic devices. Lab Chip. 2012;12[12]:2118-2134.
Rajchgot J, Thomas S, Kidd M, et al. Mobile phone–based sputum smear microscopy for tuberculosis diagnosis: A field evaluation in Uganda. Sci Rep. 2019;9[1]:11023.
Albert H, Nathavitharana RR, Isaacs C, et al. Development, roll-out, and impact of Xpert MTB/RIF for tuberculosis: What lessons have we learned? Eur Respir J. 2016;48[2]:516-525.
Marais BJ, Graham SM, Maeurer M, et al. Progress and challenges in childhood tuberculosis. Lancet Infect Dis. 2013;13[4]:287-289.
Sun AY, den Boon S, Shah M, et al. Xpert MTB/RIF Ultra for tuberculosis testing in children: A systematic review and meta-analysis. Eur Respir J. 2022;60[4]:2103198.
Dheda K, Gumbo T, Maartens G, et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017;5[4]:291-360.
Broger T, Nicol MP, Szekely R, et al. Diagnostic accuracy of a novel lateral flow lipoarabinomannan assay for tuberculosis in people with HIV. Eur Respir J. 2020;55[2]:1902124.
Xu P, Syaifullah A, Xu J. Artificial intelligence in tuberculosis diagnostics: A review of current progress and future prospects. Front Microbial. 2021; 12:661102.
Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study. Lancet Infect Dis. 2015;15[10]:1193-1202.