Evidence map›Paper›PMID 38017571›Full record

ReviewMilitary Medical Research2023

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.

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

Open access · diamondAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Military Medical Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
11.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

  1. Pooled it
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  7. Article
  8. Article
  9. Future Research Directions on Mycobacterium tuberculosis Proteins.Advances in experimental medicine and biology · 2026
    Review
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  11. Review
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  14. Article
  15. Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
    Article
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  17. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Lin-Sheng LiBeijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China.
Ling YangHebei North University, Zhangjiakou, 075000, Hebei, China.
Li ZhuangHebei North University, Zhangjiakou, 075000, Hebei, China.
Zhao-Yang YeHebei North University, Zhangjiakou, 075000, Hebei, China.
Wei-Guo ZhaoSenior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China. rujiong@ldy.edu.rs.
Wen-Ping GongBeijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China. gwp891015@whu.edu.cn.ORCID 0000-0002-0333-890X
Hebei North University · CNChinese PLA General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Latent TuberculosisTuberculosisArtificial IntelligenceBiomarkersEpigenesis, GeneticHumansMachine LearningBiomarkersBiomarkersDifferential diagnosisLatent tuberculosis infection (LTBI)Machine learning (ML)Tuberculosis (TB)

Identifiers

PMID38017571
PMCPMC10685516
OpenAlexW4389100023

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.