Evaluation of an adapted relative growth method by MALDI-TOF MS for rapid determination of the susceptibility of Escherichia coli to levofloxacin

Antimicrobial resistance (AMR) poses a critical global health threat. Conventional antimicrobial susceptibility testing (AST) requires 12–24 h, delaying targeted treatment. Although rapid AST approaches exist, their reliance on static measurements and single-colony analysis limits diagnostic accuracy by overlooking intrasample heterogeneity, which can produce misleading results when resistant subpopulations are present. Here, we present a novel analytical framework that integrates dynamic MALDI-TOF MS spectral analysis, the robust dynamic relative growth (RBD-RG) algorithm, and machine learning. This framework decodes the mass spectral evolution of Escherichia coli exposed to levofloxacin, enabling the rapid determination of levofloxacin susceptibility. AI MedLab MS was developed and validated using 60 clinical E. coli isolates, achieving robust classification performance across resistance phenotypes. Prospective validation with 50 independent isolates confirmed clinical utility: AI MedLab MS delivered AST results within 2 h, with an overall accuracy of 84.0% and a precision of 95.8% for resistant isolates. By shifting the analytical paradigm from single-timepoint detection to dynamic trajectory modeling, this study demonstrates a precise and interpretable methodology for rapid AST. The platform addresses key limitations of conventional methods by analyzing the overall bacterial population rather than individual colonies, thereby improving classification accuracy for complex resistance phenotypes, and has the potential to provide actionable clinical decision-making within a 2-h window.