Integrating multi-species and multi-antibiotic resistance classification with MALDI-TOF: a deep learning approach to predict AMR

Abstract

Antimicrobial resistance (AMR) poses a significant global health threat, impacting clinical treatments, agriculture, and public health. Although mass spectrometry techniques like MALDI-TOF provide opportunity for rapid AMR detection, current state-of-the-art models, such as MSDeepAMR, are limited to single-label classification, requiring separate models for each bacterium-antibiotic combination. These approaches struggle with challenges such as class imbalance, incomplete labels, and poor generalization across bacterial strains and antibiotics. This study addresses these limitations by introducing a novel multi-label, multi-bacteria classification framework (MLMBC) that simultaneously predicts AMR across multiple bacterial species and antibiotics using MALDI-TOF mass spectrometry data and Convolutional Neural Networks (CNNs), hereby establishing a solid foundation for the development of robust and rapid diagnostic tools to address the growing threat of multidrug-resistant bacteria. Utilizing CNNs in combination with transfer learning and semi-supervised classification techniques, such as self-training, pseudo-labeling and kNN label propagation, our approach improves accuracy and overcomes dataset limitations. Results demonstrate that the proposed approach with pseudo-labeling consistently outperforms single- and multi-label baselines; for example, it achieves AUROC \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document} 0.94 for E. coli, K.pneumoniae and S.aureus - Ceftriaxone pairs. Statistical tests support the effectiveness of the proposed approach, demonstrating that simultaneous multi label, multi-bacteria modeling constitutes a reliable and scalable strategy for antibiotic resistance prediction in clinically relevant settings.