Urological diagnostics based on kidney stone detection in CT imaging using YOLOv8 deep learning framework.

Introduction: Kidney stone disease is a common urological condition requiring timely detection to prevent complications. Non-contrast computed tomography (CT) is the gold standard for detecting renal calculi, but manual interpretation is time-consuming and subject to variability. Methods: This study evaluates four deep learning object detection models-YOLOv8, YOLOv5, Faster R-CNN, and RetinaNet-for automated kidney stone detection in CT images. A dataset of 4,000 annotated CT slices from 170 patients was used. Performance was evaluated using mAP@0.5, precision, recall, false positive and false negative rates, and inference speed. Results: Faster R-CNN achieved the highest localization accuracy (mAP@0.5 = 0.93), while YOLOv8 demonstrated the best balance between accuracy (mAP@0.91) and computational efficiency, achieving real-time inference at 65 FPS. Discussion: The results highlight the trade-off between detection accuracy and processing speed across architectures. YOLOv8 provides an optimal balance for clinical implementation due to its strong performance and real-time capability.