Beijing 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.
Hebei North University, Zhangjiakou, 075000, Hebei, China.
Mil Med Res. 2023 Nov 28;10(1):58. doi: 10.1186/s40779-023-00490-8.
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.
潜伏性结核感染 (LTBI) 已成为活动性结核病 (ATB) 的主要来源。虽然结核菌素皮肤试验和干扰素-γ释放试验可用于诊断 LTBI，但这些方法只能区分感染个体和健康个体，而不能区分 LTBI 和 ATB。因此，LTBI 的诊断面临许多挑战，例如缺乏用于区分 LTBI 的有效结核分枝杆菌 (MTB) 生物标志物、来自宿主的生物标志物的诊断效果低，以及缺乏区分 LTBI 和 ATB 的金标准。痰培养作为诊断结核病的金标准，耗时且不能区分 ATB 和 LTBI。本文综述了 MTB 的发病机制和 LTBI 宿主的免疫机制，包括先天和适应性免疫反应、MTB 的多种免疫逃避机制以及表观遗传调控。在此基础上，总结了 LTBI 诊断的现状和挑战，介绍了机器学习 (ML) 在 LTBI 诊断中的应用，以及 ML 在这方面的优势和局限性。最后，讨论了 ML 在 LTBI 诊断中的未来发展方向。
