Research progress on the detection techniques and diagnosis of latent tuberculosis infection
CHEN Guozhi1, ZHAO Lina2
1. Yueqing Center for Disease Control and Prevention (Yueqing Institute of Public Health Supervision), Yueqing, Zhejiang 325600, China; 2. Wenzhou Center for Disease Control and Prevention (Wenzhou Institute of Public Health Supervision), Wenzhou, Zhejiang 325000, China
Abstract：Latent tuberculosis infection (LTBI) refers to a specific immune state after Mycobacterium tuberculosis infection, affecting approximately one quarter of the global population and contributing to 85%-90% of active pulmonary tuberculosis cases. At present, the tuberculin skin test and interferon-gamma release assay are the main detection methods for LTBI, yet both have certain limitations, and there is no unified standard for LTBI diagnosis. With the rapid advancement of molecular biotechnology and artificial intelligence, technologies such as RNA sequencing, proteomics and machine learning have opened up new perspectives for LTBI diagnosis. RNA sequencing and proteomics can reveal the complexity and diversity of gene and protein expression profiles under LTBI status, and improve diagnostic accuracy by identifying specific biomarkers. Machine learning can establish diagnostic models through algorithm training, so as to provide rapid diagnosis and personalized decision support for LTBI screening. This paper reviews the current diagnostic situation, novel detection techniques of LTBI and the application of machine learning in auxiliary diagnosis of LTBI, aiming to provide theoretical references for the diagnosis of LTBI.
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