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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Manual urine sediment analysis is crucial but suffers from subjectivity and time constraints.
  • Existing automated methods lack the granularity for comprehensive particle classification.
  • Developing an objective and efficient automated system is vital for clinical practice.

Purpose of the Study:

  • To develop a novel deep learning method for automated and reliable urine sediment analysis.
  • To enable simultaneous, fine-grained classification of diverse urine sediment particles.
  • To overcome limitations of manual analysis and prior automated techniques.

Main Methods:

  • Utilized a multi-head YOLOv12 architecture with self-supervised pretraining.
  • Implemented Slicing Aided Hyper Inference (SAHI) for advanced object detection.
  • Created a large-scale dataset (OpenUrine) with 790 labeled and 5640 unlabeled images across 39 categories.

Main Results:

  • Achieved 76.59% precision and 64.15% mean Average Precision (mAP) on a 39-class dataset.
  • Demonstrated competitive detection accuracy, particularly for small and low-contrast objects.
  • The six specialized detection heads enabled comprehensive particle classification.

Conclusions:

  • The proposed deep learning method offers a significant advancement in automated urine sediment analysis.
  • This AI-driven approach enhances diagnostic capabilities for renal and urinary disorders.
  • The model's performance indicates its potential for clinical application and improved patient outcomes.