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

## TL;DR

This paper explores using deep learning models to automatically detect kidney stones in CT scans, finding that YOLOv8 offers the best balance of accuracy and speed for clinical use.

## Contribution

The study introduces a comparative evaluation of four deep learning models for kidney stone detection in CT imaging, emphasizing YOLOv8's real-time performance and accuracy.

## Key findings

- Faster R-CNN achieved the highest localization accuracy (mAP@0.5 = 0.93).
- YOLOv8 provided the best balance of accuracy (mAP@0.91) and speed (65 FPS).
- YOLOv8 is optimal for clinical use due to its real-time inference capability.

## Abstract

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.

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.

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.

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.