From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning
From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning
- MMR 2024年11卷第5期 页码：747-784
- Affiliations：
1.Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
2.Hebei North University, Zhangjiakou 075000,Hebei, China
3.Senior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
- Author bio：
*rujiong@ldy.edu.rs;
gwp891015@whu.edu.cn
- Funds：
DOI：10.1186/s40779-023-00490-8
中图分类号：-
纸质出版：2024-10
- Accepted：
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From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning[J]. MMR, 2024,11(5):747-784.
Cite this article as: Li LS, Yang L, Zhuang L, Ye ZY, Zhao WG, Gong WP. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning. Mil Med Res. 2023;10(1):58.
From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning[J]. MMR, 2024,11(5):747-784. DOI： 10.1186/s40779-023-00490-8.
Cite this article as: Li LS, Yang L, Zhuang L, Ye ZY, Zhao WG, Gong WP. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning. Mil Med Res. 2023;10(1):58. DOI： 10.1186/s40779-023-00490-8.
摘要
Abstract
Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI
these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus
the diagnosis of LTBI faces many challenges
such as the lack of effective biomarkers from
Mycobacterium tuberculosis
(MTB) for distinguishing LTBI
the low diagnostic efficacy of biomarkers derived from the human host
and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture
as the gold standard for diagnosing tuberculosis
is time-consuming and cannot distinguish between ATB and LTBI. In this article
we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI
including the innate and adaptive immune responses
multiple immune evasion mechanisms of MTB
and epigenetic regulation. Based on this knowledge
we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis
as well as the advantages and limitations of ML in this context. Finally
we discuss the future development directions of ML applied to LTBI diagnosis.
关键词
Keywords
references
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