From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | Military Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40779-023-00490-8 |
| _version_ | 1797415717677563904 |
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| author | Lin-Sheng Li Ling Yang Li Zhuang Zhao-Yang Ye Wei-Guo Zhao Wen-Ping Gong |
| author_facet | Lin-Sheng Li Ling Yang Li Zhuang Zhao-Yang Ye Wei-Guo Zhao Wen-Ping Gong |
| author_sort | Lin-Sheng Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:52:33Z |
| format | Article |
| id | doaj.art-bb1befb987fd491f950b4e6c4cd83085 |
| institution | Directory Open Access Journal |
| issn | 2054-9369 |
| language | English |
| last_indexed | 2024-03-09T05:52:33Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Military Medical Research |
| spelling | doaj.art-bb1befb987fd491f950b4e6c4cd830852023-12-03T12:16:06ZengBMCMilitary Medical Research2054-93692023-11-0110113710.1186/s40779-023-00490-8From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learningLin-Sheng Li0Ling Yang1Li Zhuang2Zhao-Yang Ye3Wei-Guo Zhao4Wen-Ping Gong5Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General HospitalHebei North UniversityHebei North UniversityHebei North UniversitySenior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General HospitalBeijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General HospitalAbstract 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.https://doi.org/10.1186/s40779-023-00490-8Tuberculosis (TB)Latent tuberculosis infection (LTBI)Machine learning (ML)BiomarkersDifferential diagnosis |
| spellingShingle | Lin-Sheng Li Ling Yang Li Zhuang Zhao-Yang Ye Wei-Guo Zhao Wen-Ping Gong From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning Military Medical Research Tuberculosis (TB) Latent tuberculosis infection (LTBI) Machine learning (ML) Biomarkers Differential diagnosis |
| title | From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning |
| title_full | From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning |
| title_fullStr | From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning |
| title_full_unstemmed | From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning |
| title_short | From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning |
| title_sort | from immunology to artificial intelligence revolutionizing latent tuberculosis infection diagnosis with machine learning |
| topic | Tuberculosis (TB) Latent tuberculosis infection (LTBI) Machine learning (ML) Biomarkers Differential diagnosis |
| url | https://doi.org/10.1186/s40779-023-00490-8 |
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