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

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

作者机构：Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and TreatmentSenior Department of Tuberculosisthe Eighth Medical Center of PLA General HospitalBeijing100091China Hebei North UniversityZhangjiakou075000HebeiChina Senior Department of Respiratory and Critical Care Medicinethe Eighth Medical Center of PLA General HospitalBeijing100091China 

出 版 物：《Military Medical Research》 (军事医学研究（英文版）)

年 卷 期：2024年第11卷第5期

页      面：747-784页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

主　　题：Tuberculosis(TB) Latent tuberculosis infection(LTBI) Machine learning(ML) Biomarkers Differential diagnosis 

摘      要：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 ***,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 *** culture,as the gold standard for diagnosing tuberculosis,is time-consuming and cannot distinguish between ATB and *** 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 *** 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 ***,we discuss the future development directions of ML applied to LTBI diagnosis.