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CoronaryDominance: Angiogram dataset for coronary dominance classification.

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A new dataset of 1,574 invasive coronary angiograms aids coronary dominance classification for assessing coronary artery disease severity. This resource supports accurate SYNTAX score estimation and uncertainty analysis.

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Machine Learning

Background:

  • Coronary artery disease (CAD) severity assessment is crucial for treatment planning.
  • SYNTAX score estimation relies on accurate coronary dominance classification.
  • Existing datasets may lack diversity or specific classifications for robust model training.

Purpose of the Study:

  • To introduce a comprehensive dataset for coronary dominance classification.
  • To facilitate research in coronary artery disease severity assessment using invasive coronary angiograms.
  • To enable the development of models for accurate SYNTAX score estimation.

Main Methods:

  • A dataset of 1,574 invasive coronary angiogram studies was curated.
  • Studies are X-ray multi-view videos classified as right or left dominant.
  • The dataset includes three parts: main (with 5 categories including RCA occlusion), real distribution, and domain shift.

Main Results:

  • The dataset provides detailed classifications for 1,574 angiographic studies.
  • Includes categories for normal, poor quality, artifact, high uncertainty, and right coronary artery (RCA) occlusion.
  • The dataset is structured into main, real distribution, and domain shift subsets for varied testing.

Conclusions:

  • This dataset advances coronary dominance classification research.
  • It supports improved accuracy in SYNTAX score estimation and CAD severity assessment.
  • The diverse subsets facilitate robust model evaluation and generalization.