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 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.

Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection

“…Assessing IL-5 levels relative to other cytokines in response to HP16118P can help identify the stage of Mycobacterium tuberculosis infection. Nonetheless, IL-5 should be analyzed alongside a comprehensive cytokine profile for an accurate diagnosis [ 68 , 69 ]. Further research is necessary to fully understand IL-5’s diagnostic role in TB infection.…”

“…As machine learning (ML) becomes integral in diagnosing TB, its use in differentiating LTBI from ATB remains limited [ 69 , 73 ]. Our study aimed to address this gap by comparing traditional ROC methods with ML in diagnosing latent infections.…”

Application of artificial intelligence in diagnosis of pulmonary tuberculosis

“…Research found that the sensitivity, speci city, positive predictive value, negative predictive value (NPV), and diagnostic accuracy of the AI-assisted system were higher than those of the physician alone. [1] Another study compared the performance of AI automated image reading technology and manual image reading in identifying pathogen-positive PTB patients, and the results showed that among 47 pathogen-positive PTB patients, 14 cases were misdiagnosed by senior experts and 5 cases were misdiagnosed by AI. [2] These data suggested that the sensitivity of AI reading was approximately 20% higher than that of human reading.…”

Visualization analysis of research progress and trends in coexistence of lung cancer and pulmonary tuberculosis using bibliometrics

“…a gradually declining trend in the prevalence rates of TB, challenges remain in effectively controlling and eradicating PTB, particularly in rural and underdeveloped western regions [4,[57][58][59][60]. Additionally, the incidence of LC in China is on the rise, mainly attributable to factors such as smoking, air pollution, and occupational hazards [61].…”

“…Similarly, according to statistical data released by the China National Cancer Center in 2022, there were 2.414 million cancer deaths in China in 2016, with LC being the leading cause, accounting for 65.7% of the total [3]. Pulmonary tuberculosis (PTB) is a global infectious disease caused by Mycobacterium tuberculosis (MTB) infection [4]. The Global Tuberculosis Report released by the World Health Organization (WHO) in 2023 states that approximately 7.5 million new patients were diagnosed with TB globally in 2022, marking the highest number since the WHO began in 1995 [5].…”