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Related Concept Videos

Atherosclerosis I: Introduction01:30

Atherosclerosis I: Introduction

596
Atherosclerosis is a progressive disorder characterized by the buildup of plaques on the arterial inner wall, causing them to narrow and harden over time. These plaques comprise lipids, calcium, blood components, carbohydrates, and fibrous tissue. The process primarily affects the intima of large and medium-sized arteries, reducing blood flow in any artery.Etiology and risk factorsThe cause of atherosclerosis is multifactorial, involving a complex interplay among endothelial injury, lipid...
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Atherosclerosis II: Clinical Manifestations and Diagnostic Tests01:27

Atherosclerosis II: Clinical Manifestations and Diagnostic Tests

330
Atherosclerosis is a progressive disorder that leads to the thickening and narrowing of arterial walls due to plaque buildup. This condition can cause various symptoms depending on the arteries affected:Coronary Artery Disease (CAD): This condition affects the coronary arteries and may lead to chest pain (angina), shortness of breath (dyspnea), heart attacks, and other heart disease symptoms.Cerebrovascular Disease: This affects blood flow to the brain, causing transient ischemic attacks (TIAs)...
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Atherosclerosis III: Management01:26

Atherosclerosis III: Management

251
Management of atherosclerosis involves an integrated strategy encompassing pharmacological treatment, surgical interventions, lifestyle changes, and nutrition therapy to address the multifactorial nature of the disease.Pharmacological TherapyA cornerstone of atherosclerosis management is the use of pharmacological agents. Statins, such as atorvastatin, are pivotal in inhibiting HMG-CoA reductase, an enzyme that catalyzes an initial step in cholesterol synthesis in the liver. This reduction in...
251

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Related Experiment Video

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A Human Ex Vivo Atherosclerotic Plaque Model to Study Lesion Biology
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Atherosclerotic Plaque Tissue Characterization: An OCT-Based Machine Learning Algorithm With ex vivo Validation.

Chunliu He1, Zhonglin Li2, Jiaqiu Wang3

  • 1School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.

Frontiers in Bioengineering and Biotechnology
|July 28, 2020
PubMed
Summary
This summary is machine-generated.

A new machine learning algorithm accurately characterizes atherosclerotic plaque components using optical coherence tomography (OCT) imaging. This breakthrough improves plaque vulnerability assessment and standardizes OCT interpretation for better cardiovascular care.

Keywords:
atherosclerotic plaquecarotid arteryhistologymachine learningoptical coherence tomography

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Area of Science:

  • Cardiovascular Imaging
  • Medical Artificial Intelligence
  • Biomedical Engineering

Background:

  • Standardizing optical coherence tomography (OCT) interpretation of plaque morphology is crucial for assessing cardiovascular risk.
  • Current methods for quantitative assessment of plaque vulnerability using OCT require enhanced accuracy and efficiency.
  • Developing validated algorithms for plaque characterization is essential for clinical application.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for characterizing atherosclerotic plaque components using intravascular OCT.
  • To improve the accuracy and efficiency of OCT imaging in quantitative plaque vulnerability assessment.
  • To enable standardization of OCT image interpretation for carotid plaque morphology.

Main Methods:

  • A machine learning algorithm was applied to ex vivo carotid plaque tissue samples from 31 patients.
  • Intravascular OCT imaging was used to acquire plaque data.
  • Optical parameters, texture features, and pixel positions were extracted for tissue characterization.
  • Sensitivity, specificity, and accuracy were quantified to assess feature set performance.

Main Results:

  • The algorithm achieved pixel-wise classification accuracies of 80.0% for fibrous tissue, 62.0% for calcified tissue, and 83.1% for lipid tissue compared to histology.
  • Combined feature sets demonstrated excellent accuracy in characterizing plaque components.
  • Classification accuracy was lower for calcified tissue compared to fibrous and lipid tissues.

Conclusions:

  • The developed machine learning algorithm effectively characterizes atherosclerotic plaque components using intravascular OCT.
  • The algorithm shows significant potential for standardizing OCT interpretation and improving plaque vulnerability assessment.
  • This approach offers a promising tool for quantitative analysis of carotid plaque morphology.