ArticleJournal of clinical microbiology2025
Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.
Article in Journal of clinical microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 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
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.Health science reports · 2026Article
- Bacterial heteroresistance mechanisms, dynamics, and emerging diagnostic approaches.Bioscience reports · 2026Review
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase).Nature communications · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Non-Targeted Screening Method for Detecting Temporal Shifts in Spectral Patterns of Methicillin-ResistantMicroorganisms · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Rapid prediction of vancomycin-resistantFrontiers in microbiology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Matrix-assisted laser desorption-ionization-time of flight (MALDI-TOF) mass spectra can be used to predict antimicrobial resistance (AMR) using machine learning (ML). This study aimed to validate the performance of ML models for AMR prediction using own and publicly available MALDI-TOF data and to test how these models perform over time. Mass spectra of IMPORTANCE: MALDI-TOF mass spectrometry can be used not only for bacterial species identification but also for the prediction of antimicrobial resistance (AMR) using machine learning (ML). Such an approach would provide antimicrobial susceptibility test results one day earlier than conventional routine diagnostics. This is essential for an early targeted treatment to reduce mortality of severe infections. We show that the performance of ML for the prediction of AMR based on MALDI-TOF data is good (AUROC ≥ 0.8). However, the ML models need to be trained on local data and retrained regularly to maintain a good performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.