ArticleJournal of mass spectrometry and advances in the clinical lab2025
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant
Article in Journal of mass spectrometry and advances in the clinical lab, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Can Artificial Intelligence Transform Early Warning for Antimicrobial-Resistant Outbreak Clones? Approaches, Gaps, and Opportunities: A Scoping Review.Antibiotics (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Carbapenem-resistant Methods: Machine learning classifiers were applied using MALDI-TOF MS data to discriminate KPC-type, NDM-type CRKP, and carbapenem-susceptible strains (CSKP). Model performance was validated across platforms and strain collections. SHapley Additive exPlanations (SHAP) analysis and phylogenetic reconstruction were used to interpret feature contributions and genetic determinants. Results: Significant spectral divergence was observed among Conclusion: This study developed models to classify KPC, NDM, and CSKP strains using MALDI-TOF MS combined with machine learning. KPC strains were effectively classified across platforms, whereas NDM and CSKP strains showed limited differentiation due to their close evolutionary relationship. Effective classification requires consideration of regional strain variation and periodic model updates informed by local epidemiology.
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Registered trials
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