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A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography

Alberto Boi1, Ankush D Jamthikar2, Luca Saba3

  • 1Department of Cardiology, University of Cagliari, Cagliari, Italy.

Current Atherosclerosis Reports
|May 22, 2018
PubMed

Insights

Accurately identifying atherosclerotic plaque components using optical coherence tomography (OCT) is key for cardiovascular disease (CVD) risk stratification. Machine learning and deep learning combined offer the most promising approach for improved OCT-based risk assessment.

Area of Science:

  • Cardiovascular Imaging and Diagnostics
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Atherosclerotic plaque buildup in coronary arteries causes stenosis and severe cardiovascular events.
  • Accurate identification and quantification of plaque components are crucial for cardiovascular disease (CVD) risk assessment and stratification.
  • High-resolution imaging techniques are essential for characterizing complex coronary plaque components.

Purpose of the Study:

  • To compare various methodologies for plaque tissue characterization, classification, and arterial measurements using optical coherence tomography (OCT).
  • To present different approaches for predicting and stratifying CVD risk based on OCT plaque characterization.
  • To identify and evaluate three dominant paradigms for atherosclerotic plaque characterization.

Main Methods:

  • Review and condensation of 126 papers focusing on OCT-based plaque characterization.
  • Comparison of methodologies including optical attenuation coefficients, machine learning algorithms, and deep learning techniques.
  • Evaluation of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) for plaque visualization.

Main Results:

  • Optical coherence tomography (OCT) offers superior visualization of coronary vessel wall layers compared to intravascular ultrasound (IVUS).
  • Three dominant paradigms exist for plaque characterization: optical attenuation, machine learning, and deep learning.
  • A combination of machine learning and deep learning techniques shows the greatest potential for enhanced OCT-based risk stratification.

Conclusions:

  • Accurate characterization of atherosclerotic plaque components via OCT is vital for CVD risk stratification.
  • Machine learning and deep learning approaches significantly improve the accuracy of OCT-based plaque analysis.
  • Integrated machine learning and deep learning strategies represent the optimal solution for advanced OCT-based cardiovascular risk assessment.
Abstract

Keywords:
AtherosclerosisCardiovascular diseaseCoronaryMachine learning and deep learningOptical coherence tomographyPlaque characterizationRisk stratification

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