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

Yuguang Ye1,2,3, Kavimbi Chipusu4, Liuying He5

  • 1School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, China.

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Summary
This summary is machine-generated.

This study compared deep learning models for kidney stone detection in CT scans. YOLOv8 offers the best balance of accuracy and speed for clinical use.

Keywords:
YOLOv8clinical decision supportcomputed tomographykidney stonesmedical image analysisobject detection

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Area of Science:

  • Urology
  • Radiology
  • Artificial Intelligence

Background:

  • Kidney stone disease is a prevalent urological condition.
  • Accurate and timely detection of renal calculi is crucial to prevent complications.
  • Non-contrast computed tomography (CT) is the standard for kidney stone detection, but manual interpretation is challenging.

Purpose of the Study:

  • To evaluate the performance of four deep learning object detection models (YOLOv8, YOLOv5, Faster R-CNN, RetinaNet) for automated kidney stone detection in CT images.
  • To compare the accuracy and computational efficiency of these models.

Main Methods:

  • A dataset of 4,000 annotated CT slices from 170 patients was utilized.
  • Performance was assessed using metrics including mAP@0.5, precision, recall, false positive/negative rates, and inference speed.

Main Results:

  • Faster R-CNN achieved the highest localization accuracy with an mAP@0.5 of 0.93.
  • YOLOv8 demonstrated a strong balance of accuracy (mAP@0.91) and computational efficiency, achieving real-time inference at 65 FPS.

Conclusions:

  • Deep learning models show promise for automated kidney stone detection in CT images.
  • YOLOv8 presents an optimal solution for clinical implementation, offering a favorable trade-off between detection accuracy and real-time processing capabilities.