ReviewFrontiers in microbiology2025
Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges.
Review in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Current Perspectives onInternational journal of molecular sciences · 2026Review
- Machine learning methods identified cellular senescence-related hub molecules in sepsis-induced acute respiratory distress syndrome (ARDS) and their upstream regulatory network.Molecular biology reports · 2026Article
- Metabolomics and Artificial Intelligence (AI) assisted metabolomics in the diagnosis of non-tuberculous mycobacterial infections: progress and challenges.New microbes and new infections · 2026Review
- [Pulmonary disease caused byZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Zoonotic Nontuberculous Mycobacteria: Transmission Pathways, Laboratory Diagnosis, Detection Methodologies, and One Health Priorities.Infectious diseases & clinical microbiology · 2026Review
- Non-tubercular mycobacterial infections: Emerging from the shadows.Lung India : official organ of Indian Chest Society · 2026Article
- Mycobacterium cajalii sp. nov., a novel scotochromogenic rapid-growing nontuberculous mycobacterial species closely related to Mycobacterium servetii.Antonie van Leeuwenhoek · 2026Article
- Understanding recurrence inJournal of clinical microbiology · 2026Review
- Advanced diagnostic methods for nontuberculous mycobacterial infections.Frontiers in tuberculosis · 2026Review
- Current Advances in Developing New Antimicrobial Agents Against Non-TuberculousAntibiotics (Basel, Switzerland) · 2025Review
- Bidirectional pathogenesis between non-tuberculous mycobacteria and bronchiectasis: clinical insights, diagnostic challenges and future directions-Perspectives from South Asia.Frontiers in tuberculosis · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The nontuberculous mycobacterial (NTM) infections cause morbidity and mortality in individuals who are immunocompromised and those with lung conditions. The timely diagnosis of NTM infections is thus the need of the hour for appropriate management of the disease. In this context, genomics has played a pivotal role in diagnosis of NTM by targeting various conserved regions which are useful for species identification and diagnosis. Also, the exploring of whole genome of nontuberculous mycobacteria has made species identification easier and has revolutionized the diagnostic landscape of NTM. The refinement of Whole Genome Sequencing (WGS) and the advent of targeted Next Generation Sequencing (tNGS) and metagenomic NGS (mNGS) has helped in bringing down the cost without compromising the quality in NTM diagnostics. The advent of artificial intelligence (AI) technologies has made NTM diagnosis even easier by analyzing complex genomic data and providing faster results. Thus, this comprehensive review discusses the strides made in genomics and AI based approaches in the diagnosis of NTM infections and the way forward for harnessing this potential to the maximum for the benefit of mankind.
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