Abstract:

Non-tuberculous mycobacteria (NTM) are a group of opportunistic pathogens widely present in the environment. The global incidence of NTM infections and associated drug resistance rates are increasing. Although unified global surveillance data are lacking, studies based on national surveillance or large-scale databases from multiple countries have consistently reported a significant increasing trend in the incidence of NTM infections. In Japan, the incidence of pulmonary disease caused by NTM infections increased from 15.8 to 19.2 cases per 100 000 population between 2013 and 2017, representing an increase of 17.7%. In Denmark, a national registry study covering 1991 to 2022 shows that the incidence of NTM-related pulmonary diseases has continuously increased over the 30-year period, with an average annual growth rate of 2.3%. The clinical manifestations of non-tuberculous mycobacterial pulmonary disease are non-specific and highly similar to those of pulmonary tuberculosis, but the treatment strategies are markedly different. Moreover, treatment schemes vary greatly among different NTM species. Therefore, rapid and accurate laboratory identification and species differentiation are essential. Conventional diagnostic methods such as smear microscopy and culture have technical limitations including low sensitivity and long turnaround time, making it difficult to meet the requirements of early and precise clinical diagnosis and treatment. Molecular diagnostic techniques, represented by real-time fluorescent PCR, have greatly shorte-ned detection duration and enabled rapid screening and differentiation of NTM. Genomic technologies, represented by next-generation sequencing and nanopore sequencing, can achieve integrated and precise identification of bacterial species to the subspecies level and detect drug resistance genes directly from clinical specimens, thereby guiding early clinical medication. Artificial intelligence technology demonstrates strong potential in integrating imaging features, clinical data, and even genomic information, and can assist in auxiliary diagnosis, optimize treatment schemes, and predict prognosis. The development of these novel NTM laboratory diagnostic techniques is driving the transformation of NTM diagnosis and treatment from an empirical model to an efficient, precise, and individualized model.

Key words: Non-tuberculous mycobacteria, Molecular diagnostic techniques, Genome sequencing, Biomarkers, Artificial intelligence