ArticleNature communications2025
CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion tests.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- 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
- Development of a predictive model using automated machine learning for Carbapenem-resistant Organisms (CRO) infections in hospitalized patients.Frontiers in microbiology · 2026Article
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Authors and funding
9 authors.
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Abstract
Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carbapenemase production from inhibition zone diameters of eight antibiotics, using 385 isolates for training with whole genome sequencing as reference. Validation on two external datasets (A = 282, B = 518 isolates) shows high performance: sensitivity/specificity are 96.6%/84.4% (training), 96.3%/86.1% (A), and 91.2%/87.0% (B, five antibiotics). In contrast, the algorithms of EUCAST and the Antibiogram Committee of the French Society of Microbiology (CA-SFM) exhibit lower specificity (8.2% and 40.1%, respectively on the training dataset). In this work, we show that CarbaDetector, available as a web-app, reduces unnecessary confirmatory testing and accelerates the time to result. This approach offers high sensitivity and improved specificity compared to standard algorithms and has the potential to improve CPE detection, especially in resource-limited settings.
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