Frontiers in Cardiovascular Medicine (Nov 2019)

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

  • Nils Hampe,
  • Nils Hampe,
  • Nils Hampe,
  • Jelmer M. Wolterink,
  • Jelmer M. Wolterink,
  • Jelmer M. Wolterink,
  • Sanne G. M. van Velzen,
  • Tim Leiner,
  • Ivana Išgum,
  • Ivana Išgum,
  • Ivana Išgum,
  • Ivana Išgum

DOI
https://doi.org/10.3389/fcvm.2019.00172
Journal volume & issue
Vol. 6

Abstract

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Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.

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