From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning

Lin-Sheng Li; Ling Yang; Li Zhuang; Zhao-Yang Ye; Wei-Guo Zhao; Wen-Ping Gong

doi:10.1186/s40779-023-00490-8

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From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning

REVIEW | Updated：2025-12-13

- From immunology to artificial intelligence: revolu-tionizing latent tuberculosis infection diagnosis with machine learning
- Military Medical Research Vol. 11, Issue 5, Pages: 747-784(2024)
- 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
  CLC：
- Published：2024-10
- Accepted：
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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：

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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Related Institution

Institute of Translational Medicine, Organoid Research Center, National Center for Translational Medicine (Shanghai), Shanghai University Branch

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Department of Orthopedics, Shanghai Zhongye Hospital

Department of Orthopedics, Trauma Orthopedics Center, Institute of Musculoskeletal Injury and Translational Medicine of Organoids, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine

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