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An Unsupervised Learning Algorithm for the Automatic Classification of Coronary Artery Lesions.

Julia Szopinska1, Piotr A Regulski1, Maciej Mazurek2

  • 1Department of Dental and Maxillofacial Radiology, Laboratory of Digital Imaging and Virtual Reality, Medical University of Warsaw, Warsaw, POL.

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Summary

This study introduces an unsupervised clustering method for classifying coronary artery lesions from CT scans, improving accuracy without manual annotations. This approach aids in diagnosing coronary artery disease (CAD) more efficiently.

Keywords:
atherosclerotic plaque classificationclustering algorithmscomputed tomographycoronary artery diseasevessel segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Research

Background:

  • Coronary artery disease (CAD) is a leading global cause of mortality.
  • Accurate CT-based identification of coronary artery lesions is crucial for patient management.
  • Current supervised methods for lesion identification are labor-intensive and prone to errors due to manual annotation requirements.

Purpose of the Study:

  • To develop and evaluate a novel unsupervised clustering method for automatic classification and characterization of coronary artery lesions from CT images.
  • To address the limitations of supervised and semi-supervised approaches by eliminating the need for manual annotations.
  • To provide a robust and efficient tool for diagnosing significant coronary artery lesions.

Main Methods:

  • Analysis of 45 coronary artery CT scans with lesions causing at least 30% stenosis.
  • Utilized nnU-Net for vessel segmentation, followed by skeletonization and feature extraction (statistical and Haralick textures).
  • Applied principal component analysis for dimensionality reduction and k-means/hybrid clustering for lesion classification, validated against fractional flow reserve (FFR).

Main Results:

  • Achieved high vessel segmentation accuracy (mean Dice coefficient of 0.93).
  • The hybrid clustering algorithm demonstrated superior performance: 95.6% sensitivity for calcified, 88.3% for mixed, and 74.1% for soft plaques.
  • Successfully identified hemodynamically significant lesions, confirmed by FFR measurements in 15 lesions.

Conclusions:

  • The proposed unsupervised clustering method effectively classifies coronary artery lesions without manual annotation.
  • The method shows potential for a practical, accurate, and efficient diagnostic approach in clinical settings.
  • External validation on larger datasets is necessary to confirm findings and facilitate clinical translation due to the small sample size and limited FFR-validated lesions.