ArticleFrontiers in microbiology2026
Automatic detection and quantification of antimicrobial inhibition zones using YOLO11n with
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: The escalating prevalence of antimicrobial resistance (AMR) constitutes a global healthcare crisis, necessitating rapid and standardized diagnostic solutions for antimicrobial susceptibility testing (AST). This study introduces an advanced, end-to-end artificial intelligence framework designed for the fully automated detection, quantification, and clinical interpretation of inhibition zones from disk diffusion assays using the state-of-the-art You Only Look Once (YOLO11n) object detection model. Methods: A high-resolution dataset of Petri dish images, featuring antibiotic discs tested against Results: Evaluation results demonstrate that the YOLO11n-based system achieved a Categorical Agreement (CA) of 94.2%, with a Very Major Error (VME) rate of 1.2% and a Major Error (ME) rate of 1.8%, performing well within clinical safety thresholds. High spatial accuracy was confirmed by a correlation coefficient of Discussion: Grad-CAM analysis confirmed that the architecture's attention is consistently aligned with biologically relevant inhibition boundaries rather than background artifacts. By providing an objective, reproducible, and transparent method for digital antibiogram analysis, this framework offers significant potential for seamless integration into clinical microbiology workflows and large-scale AMR monitoring programs.
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