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REVIEW article
Front. Cell. Infect. Microbiol.
Sec. Clinical Microbiology
Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1567592
This article is part of the Research Topic Unveiling Distinctions: Active Tuberculosis versus Latent Tuberculosis Infection - Immunological Insights, Biomarkers, and Innovative Approaches View all 5 articles
Recent Progress in Tuberculosis Diagnosis: Insights into Blood-Based Biomarkers and Emerging Technologies
Provisionally accepted
Yuhan Wang 
Zewei Yang 
晶晶 李 Jiawen Shen Huiru Cao Sensen Hu Yulu Du 
Yange Wang Zhongyi Yan 
Longxiang Xie 
Qiming Li Xianghui Li *
Salwa E. Gomaa *
Ji-Cheng Li *- Henan University, Kaifeng, China
Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. Recent advancements in the field of TB diagnostics have focused on the identification and utilization of blood-based biomarkers, offering a non-invasive, rapid, and scalable approach to disease detection. This review provides a comprehensive overview of the latest progress in blood-based biomarkers for TB, highlighting their potential to revolutionize diagnostic strategies. Furthermore, we explore emerging technologies such as NGS, PET-CT, Xpert and line probe assays, which have enhanced the sensitivity, specificity, and accessibility of biomarker-based diagnostics. The integration of artificial intelligence (AI) and machine learning (ML) in biomarker analysis is also examined, showcasing its potential to improve diagnostic accuracy and predictive capabilities. This review underscores the need for multidisciplinary collaboration and continued innovation to translate these promising technologies into practical, point-of-care solutions. By addressing these challenges, blood-based biomarkers and emerging technologies hold the potential to significantly improve TB diagnosis, ultimately contributing to global efforts to eradicate this devastating disease.
Keywords: Tuberculosis1, blood-based biomarkers2, Diagnostic technologies3, Artificial Intelligence4, global health5
Received: 14 Feb 2025; Accepted: 07 Apr 2025.
Copyright: © 2025 Wang, Yang, 李, Shen, Cao, Hu, Du, Wang, Yan, Xie, Li, Li, Gomaa and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xianghui Li, Henan University, Kaifeng, China
Salwa E. Gomaa, Henan University, Kaifeng, China
Ji-Cheng Li, Henan University, Kaifeng, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.