REVIEW article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1569615
This article is part of the Research TopicApplication of Multimodal Data and Artificial Intelligence in Pulmonary DiseasesView all 7 articles
Tuberculosis Diagnosis Using Artificial Intelligence: Current Trends and Future Prospects
Provisionally accepted
Onesime Onesime Mbulayi1*
Saint-Jean DJUNGU1,2Loukia AKETI1,3
Mohammed Amine KOULALI4Hanae AZZAOUI5Rim KOULALI5
Mohammed El Mzibri6
Imane Chaoui6Yahya TAYALATI7,8- 1University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 2Applied Informatics Research Center (CRIA), University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 3Cardio-pulmonary and Infectious Diseases Unit, Department of Pediatrics, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 4Laboratoire de Modelisation et Calcul Scientifique (LMCS), ENSAO, Mohammed First University, Rabat, Morocco
- 5Laboratoire Genie Electrique et Maintenance (LGEM), ESEF, Mohammed First University, Rabat, Morocco
- 6Centre Nationale de l’Energie, des Sciences et des Technologies Nucleaires (CNESTEN), Rabat, Morocco
- 7Faculty of Science, Mohammed V University, Rabat, Morocco
- 8School of Applied and Engineering Physics, Mohammed VI Polytechnic University, Ben Guerir,43150,Morocco, Rabat, Morocco
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Tuberculosis, an infectious disease caused by Mycobacterium tuberculosis, poses a major global health challenge. Despite being largely controlled for several decades, tuberculosis has experienced a resurgence in recent years. China has the second highest incidence of tuberculosis globally, with a prevalence of 459 cases per 100,000 individuals aged 15 years old. Chest radiography and pathology are essential tools for its detection and diagnosis. However, the small size and low number of tubercle bacilli make detection and identification under a microscope challenging, often resulting in low detection rates and false diagnoses. Artificial intelligence (AI) has emerged as a promising tool to improve the accuracy and sensitivity of tuberculosis detection. This review provides a comprehensive overview of the literature on the use of machine learning-based models for the automatic detection of tuberculosis bacilli, emphasizing the advantages of integrating in tuberculosis diagnosis. Understanding the onset and progression of tuberculosis is crucial to developing effective strategies for its diagnosis, treatment, and prevention.
Keywords: Tuberculosis, Mycobacterium, artificial intelligence, chest radiography, diagnosis
Received: 01 Feb 2025; Accepted: 10 Jun 2025.
Copyright: © 2025 Mbulayi, DJUNGU, AKETI, KOULALI, AZZAOUI, KOULALI, El Mzibri, Chaoui and TAYALATI. 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: Onesime Onesime Mbulayi, University of Kinshasa, Kinshasa, Democratic Republic of Congo
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