AI-driven identification and analysis of inhibition zones in disk diffusion tests with the hue contrast method

• A disk diffusion antibiotic susceptibility testing (AST) imaging system integrated with a hue contrast method was developed to improve measurement accuracy. • The hue contrast method achieved an 88 % success rate, outperforming the Hough circle method. • The imaging system automates AST interpretation and reporting results via the LINE chatbot, ensuring convenience and stability Antibiotic susceptibility testing (AST) is crucial for monitoring bacterial resistance and guiding treatment. The Kirby–Bauer disk diffusion test, a standard AST method, encounters challenges due to variability in manual measurements. We developed an automated imaging system with a reporting function for disk diffusion tests to address these issues. A disk diffusion test was carried out on Salmonella enterica with ceftiofur (CF), enrofloxacin (ER), and colistin (CT) disks. The disk images captured by a smartphone were annotated and processed on the Roboflow platform and then used to train the YOLOv7 model for text recognition. The YOLOv7 model achieved 100 % recognition accuracy for the CF and CT disks and 98 % for the ER disks. We employed the Hough circle and hue contrast methods to detect Petri dish and inhibition zone contours. The hue contrast method achieved an 88 % success rate for detecting inhibition zones, outperforming the Hough circle method, which showed a success rate ranging from 54–82 %. We compared the results obtained from the imaging system with those of manual measurements. The mean differences in the inhibition zone diameters were −1.3 mm for CF, −1.0 mm for ER, and −0.5 mm for CT for Salmonella enterica. The accuracy and consistency of the imaging system were also shown in Escherichia coli with mean differences of 0.1 mm for CF, −1.5 mm for ER, and 0.15 mm for CT, with all the data points within the 95 % limit of agreement. In conclusion, our automated system, which uploads images to a cloud server for analysis and generates AST reports via the LINE chatbot, significantly improves interpretation accuracy and reproducibility.