Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

119
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
119
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

55
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
55

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Impact of Strut Thickness for Patients Treated With Percutaneous Revascularization for Coronary Bifurcations: Insights From the BIFURCAT-ULTRA Registry.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2026
Same author

Hybrid Surgical-Catheter Epicardial Ablation of Ventricular Tachycardia: A Case Series.

Journal of clinical medicine·2026
Same author

Beta-blockers in patients with uncomplicated arterial hypertension: data from the Campania Salute Network Registry.

European heart journal. Quality of care & clinical outcomes·2026
Same author

Incidence and predictors of major arrhythmic events after myocarditis: a systematic review and meta-analysis.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology·2026
Same author

Clinical Impact of Myocardium at Risk in Transcatheter Aortic Valve Implantation.

Circulation. Cardiovascular interventions·2026
Same author

Primary Results of the SALAMANDER Registry: A Multicenter Observational Cohort Study.

Journal of the American Heart Association·2026

Related Experiment Video

Updated: Sep 12, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.0K

Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence

Michela Sperti1, Camilla Cardaci1, Francesco Bruno2

  • 1Department of Mechanical and Aerospace Engineering, Polito Med Lab, Politecnico di Torino, 10129 Torino, Italy.

Reviews in Cardiovascular Medicine
|August 8, 2025
PubMed
Summary

Artificial intelligence (AI) enhances intravascular optical coherence tomography (IVOCT) for coronary plaque analysis. AI integration improves accuracy and efficiency, but further validation is needed for widespread clinical use in cardiovascular risk prediction.

Keywords:
artificial intelligenceatherosclerotic plaqueautomated plaque characterizationclinical decision support systemsdeep learningintravascular imagingmachine learningoptical coherence tomography

More Related Videos

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
09:36

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

77
In vivo Near Infrared Fluorescence NIRF Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis
09:43

In vivo Near Infrared Fluorescence NIRF Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

Published on: August 4, 2011

18.1K

Related Experiment Videos

Last Updated: Sep 12, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.0K
A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
09:36

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

77
In vivo Near Infrared Fluorescence NIRF Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis
09:43

In vivo Near Infrared Fluorescence NIRF Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

Published on: August 4, 2011

18.1K

Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Intravascular optical coherence tomography (IVOCT) offers detailed coronary plaque characterization but faces limited clinical adoption due to manual assessment challenges.
  • Manual plaque analysis is time-consuming, error-prone, and suffers from high inter-observer variability, hindering its routine clinical application.
  • Artificial intelligence (AI) is being integrated to automate and improve the precision of IVOCT image analysis for coronary artery disease.

Purpose of the Study:

  • To provide a comprehensive overview of AI-based methods for analyzing IVOCT images of coronary arteries, focusing on plaque characterization.
  • To explore the clinical translation of AI in IVOCT, highlighting current AI-powered tools for plaque characterization.
  • To identify limitations and future directions for AI in IVOCT for enhanced clinical decision-making.

Main Methods:

  • Review of AI-based techniques, including machine learning and deep learning (e.g., convolutional neural networks), applied to IVOCT image analysis.
  • Focus on automatic feature extraction and classification of coronary atherosclerotic plaques.
  • Exploration of commercially available or clinically intended AI-powered IVOCT analysis tools.

Main Results:

  • AI, particularly deep learning, demonstrates robust performance in classifying plaque types and automating feature extraction from IVOCT images.
  • Several AI-driven tools are emerging for plaque characterization, aiming to improve efficiency and reproducibility.
  • Current AI solutions have limitations in the scope of assessable plaque features and are often restricted to specific regulatory or research settings.

Conclusions:

  • AI integration holds significant potential to transform IVOCT from a research tool into a clinical decision-making aid for coronary artery disease.
  • Further advancements, validation, and seamless integration with clinical systems are crucial for widespread adoption of AI-powered IVOCT analysis.
  • Enhanced AI-based IVOCT analysis can improve plaque characterization, support clinical decision-making, and advance cardiovascular risk prediction.