Comparative analysis of chronic progressive nephropathy (CPN) diagnosis in rat kidneys using an artificial...

With the development of artificial intelligence (AI), technologies based on machines and deep learning are being used in many academic fields. In toxicopathology, research is actively underway to analyze whole slide image (WSI)-level images using AI deep-learning models. However, few studies have been conducted on models for diagnosing complex lesions comprising multiple lesions. Therefore, this study used deep learning segmentation models (YOLOv8, Mask R-CNN, and SOLOv2) to identify three representative lesions (tubular basophilia with atrophy, mononuclear cell infiltration, and hyaline casts) of chronic progressive nephropathy of the kidney, a complex lesion observed in a non-clinical test using rats and selected an initial model appropriate for diagnosing complex lesions by analyzing the characteristics of each algorithm. Approximately 2000 images containing three lesions were extracted using 33 WSI of rat kidneys with chronic progressive nephropathy. Among them, 1701 images were divided into first and second rounds of learning. The loss and mAP50 values were measured twice to confirm the performances of the three algorithms. Loss measurements were stopped at an appropriate epoch to prevent overfitting, and the loss value decreased in the second round based on the data learned in the first round. After measuring the accuracy twice, detection using Mask R-CNN showed the highest mAP50 in all lesions among the three models and was considered sufficient as an initial model for diagnosing complex lesions. By contrast, the YOLOv8 and SOLOv2 models showed low accuracy for all three lesions and had difficulty with segmentation tasks. Therefore, this paper proposes a Mask R-CNN as the initial model for segmenting complex lesions. Precise diagnosis is possible if the model can be trained by increasing the input data, thereby providing greater accuracy in diagnosing pathological images.