Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens.

ABSTRACT: Antimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of mass spectral data generated by VITEK^® MS instruments for predicting antibiotic resistance in Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae using machine learning algorithms. Additionally, the potential of pre-trained models was assessed through transfer learning analysis. A dataset comprising 2229 mass spectra was collected, and classification algorithms, including Support Vector Machines, Random Forest, Logistic Regression, and CatBoost, were applied to predict resistance. CatBoost demonstrated a clear advantage over the other models, effectively handling complex non-linear relationships w

SUBMITTER: Lopez-Cortes XA 

PROVIDER: S-EPMC11817502 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature