Application of Machine Learning for Rapid Detection and Control of Antibiotic Resistance Using Microbiological Data
Articles in Press, Accepted Manuscript Available Online from 29 September 2025
Articles in Press, Accepted Manuscript Available Online from 29 September 2025
Antibiotic resistance in pathogenic bacteria poses a significant threat to global public health by limiting effective treatment options. Rapid and accurate detection of antibiotic resistance patterns is critical for guiding appropriate antimicrobial therapy and controlling the spread of resistant strains. In this study, we present a novel machine learning-based system designed to analyze microbiological data derived from clinical isolates, including antibiotic susceptibility profiles of bacterial strains associated with various infections. Our approach integrates conventional microbiological testing with advanced artificial intelligence algorithms to evaluate bacterial resistance and provide real-time, evidence-based recommendations for antibiotic selection. The system processes phenotypic resistance data obtained through standardized microbiological assays and employs a trained predictive model to identify resistance patterns and optimize therapeutic strategies. We demonstrate the workflow through detailed experimental protocols encompassing sample collection, microbial culture, susceptibility testing, data input into the AI platform, and interpretation of algorithmic outputs. This integrated methodology enhances the precision of antibiotic stewardship programs by enabling timely and informed decision-making in clinical settings. The presented protocol offers a reproducible framework for combining microbiological diagnostics with machine learning tools to address the pressing challenge of antibiotic resistance.
