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  11. Improving Prototypical Parts Abstraction For Case-based Reasoning Explanations Designed For The Kidney Stone Type Recognition

Improving prototypical parts abstraction for case-based reasoning explanations designed for the kidney stone type recognition

Daniel Flores-Araiza1, Francisco Lopez-Tiro2, Clément Larose3

  • 1Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Mexico.

Artificial Intelligence in Medicine
|September 24, 2025

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning model accurately identifies kidney stone types during ureteroscopy by analyzing visual features, improving upon existing methods and providing explainable results for urologists.

Area of Science:

  • Urology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate in-vivo identification of kidney stone types during ureteroscopy is crucial for efficient treatment and preventing recurrence.
  • Current visual recognition by urologists is operator-dependent and time-consuming.
  • Existing deep learning (DL) models lack transparency, failing to correlate visual features with established morphoconstitutional analysis (MCA) criteria.

Purpose of the Study:

  • To develop an interpretable deep learning (DL) model for in-vivo kidney stone type identification during ureteroscopy.
  • To ensure the model's decision-making process aligns with visual features used in biological morphoconstitutional analysis (MCA).
  • To improve classification accuracy and provide explainable insights for clinical application.

Main Methods:

Keywords:
DescriptorsEndososcopyExplainabilityFeature extraction

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  • A case-based reasoning DL model utilizing prototypical parts (PPs) was developed.
  • PPs encode visual features (hue, saturation, intensity, texture) relevant to MCA.
  • A novel loss function optimized PP generation, and local/global descriptors explained classification decisions.

Main Results:

  • The proposed DL model achieved an overall average classification accuracy of 90.37±0.6% on six common kidney stone types.
  • The model demonstrated enhanced explainability by linking visual features to MCA criteria.
  • Accuracy slightly surpassed the best existing DL models (88.2±2.1%) while offering superior interpretability.

Conclusions:

  • The interpretable DL model offers a significant advancement for in-vivo kidney stone identification in urology.
  • The model's ability to explain its decisions fosters trust and facilitates clinical adoption of AI solutions.
  • This approach bridges the gap between AI-driven analysis and traditional biological assessment of kidney stones.
Image classification
Kidney stone recognition
Prototypical parts