Automatic Detection and Quantification of Antimicrobial İnhibition Zones Using YOLO11n with Post-Hoc Interpretability Validation

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.