Automated image-based classification of rat fecal consistency using an object-detection tool

Manual classification of rat fecal consistency is widely used in gastrointestinal and pharmacological research, but it is highly inefficient and exposes researchers to allergens due to frequent defecation and continuous handling. Automated image-based analysis addresses a gap in high-throughput experimental workflows by reducing human error and improving scalability. Therefore, this study aimed to develop an artificial intelligence (AI) model using the YOLOv5-L6 object-detection framework to automate the classification of fecal consistency into four categories: normal, soft, muddy, and watery. Moreover, the efficiency and reproducibility of the proposed framework are compared with manual methods. A dataset of 95 images containing 1,667 labeled fecal samples was prepared with augmentation techniques to enhance robustness. The trained model achieved a mean average precision (mAP) of 86.2%, with class-specific average precision ranging from 75.5% to 95.4%. Compared with manual classification, which required an average of 45 ± 11.3 min, the AI-based approach completed initial classification in 53 s. Moreover, by including manual correction of misclassifications, the total time was approximately 8 min, representing an 82% reduction in processing time. These findings demonstrate that AI-based object detection can substantially improve workflow efficiency in preclinical research.