The Evaluation of Machine Learning Models using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) Spectra for the Prediction of Antibiotic Resistance in Klebsiella pneumoniae.

Antimicrobial resistance in Klebsiella pneumoniae poses a major clinical challenge, driving development in rapid, diagnostic strategies that extend beyond conventional susceptibility testing. Twenty-one studies demonstrate that using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) spectra to create machine learning (ML) models yields rapid and accurate predictions of antibiotic resistance in K. pneumoniae . Across these studies, most models focused on carbapenem resistance and achieved AUROC values consistently above 0.90, with ensemble algorithms, particularly Random Forest, XGBoost, and Light Gradient Boosting Machine and deep learning models such as Convolutional Neural Networks attaining accuracies as high as 97% and even AUROCs reaching 0.99 or higher. Sample sizes ranged from 35 to over 15,000 isolates, reinforcing the robustness of these findings across diverse clinical settings. In addition to high discrimination performance, this evaluation reports that ML models developed using MALDI-TOF MS spectra shortens diagnostic turnaround from days (48-96 hours with conventional methods) to minutes or hours, using existing MALDI‐TOF MS equipment for economical implementation. However, ML diagnostic tools remain constrained by limited external validation, and variability between different MALDI-TOF MS platforms. These limitations may restrict model generalisability and clinical translation, highlighting the need for standardised workflows and larger multi-centre evaluations.