AI-powered rapid detection of multidrug-resistant <i>Klebsiella pneumoniae</i> with informative peaks of MALDI-TOF MS.

ABSTRACT:

Motivation

Klebsiella pneumoniae is a highly virulent superbug with rising antibiotic resistance worldwide. While matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has transformed microbial identification, its application to antimicrobial resistance prediction remains underexplored, particularly for large clinical cohorts. In this study, we developed machine-learning models with feature-level interpretability using MALDI-TOF MS data to rapidly predict resistance to ciprofloxacin (CIP), cefuroxime (CXM), and ceftriaxone (CRO) in K. pneumoniae.

Results

Using more than 28 000 isolates from two hospitals, the best-performing models reached an independent test accuracy of 0.7858, with sensitivity of 0.7289 and specificity of

SUBMITTER: Lu JJ 

PROVIDER: S-EPMC12776345 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature