Diagnosis analysis of apoptotic bodies in rat liver using You Only Look Once (YOLO)v8 object detection algorithm

With advancements in high-resolution scanners and high-performance computers, the use of whole slide imaging (WSI) in digital pathology has increased. WSI scans glass slides and stores them in digital format, making them immune to damage or discoloration, and enabling remote pathology review and peer review. Additionally, with the development of artificial intelligence, research using deep learning models in pathology has become more widespread. In this study, the You Only Look Once (YOLO)v8 model was used to train artificial intelligence to detect apoptotic bodies commonly observed in rodent livers. A total of 1,558 rat liver images containing apoptotic bodies were collected and followed by labeling and data augmentation using flipping and rotation techniques to expand the dataset to 3,738 images. The dataset was then divided into training, validation, and test sets to develop and evaluate a model for object recognition. The training was conducted with an epoch set to 300. The YOLOv8 model detected apoptotic bodies with a mean average precision at 50% value of 0.882. Although the model’s accuracy for detecting individual apoptotic bodies may not seem extremely high, it is important to note that the size of apoptotic bodies is very small compared to hepatocytes, making them harder to detect. However, the model’s overall performance is expected to improves significantly with a larger dataset. The YOLOv8 model successfully detected apoptotic bodies with high accuracy. This can help reduce the workload of toxicologic pathologists and decrease the time and cost involved in pathology review. Furthermore, with an increased dataset, even higher accuracy can be expected in the future.