Deep Learning-Based Rapid Identification of Escherichia coli and Klebsiella pneumoniae from Chromogenic Agar Urine Cultures Using YOLOv12

This AI model enables rapid (sub-second), accurate phenotypic classification of the most common UTI pathogens directly from routine culture plates. Integration into automated systems could reduce the diagnostic timeline by approximately 18 hours, facilitating earlier targeted therapy and supporting antimicrobial stewardship. A key consideration for implementation is the model's basis in phenotypic identification, which aligns with standard laboratory workflow but requires awareness of rare chromogenic variants. The reliance on chromogenic morphology for <i>E. coli</i> ground truth has been a key limitation of the study.