Automatic detection and quantification of antimicrobial inhibition zones using YOLO11n with post-hoc interpretability validation

IntroductionThe 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 de...

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Auteurs principaux:	Fatih Ciftci, Azime Erarslan, Javad Rahebi
Format:	Artigo
Langue:	Inglês
Publié:	Frontiers Media S.A. 2026-04-01
Collection:	Frontiers in Microbiology
Sujets:	antimicrobial susceptibility testing categorical agreement clinical microbiology deep learning Grad-CAM inhibition zone measurement
Accès en ligne:	https://www.frontiersin.org/articles/10.3389/fmicb.2026.1810754/full
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