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

You might also read

Related Articles

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

Sort by
Same author

Effectiveness and safety of a shortened oral regimen for rifampicin- or multidrug-resistant TB.

IJTLD open·2026
Same author

Two-Tiered Grading in Required Clerkships: Understanding the Why and Results of Making This Change.

Cureus·2026
Same author

Radiometer calibration using machine learning.

Scientific reports·2025
Same author

A Feasible Simplified Pulmonary Ultrasound Scoring System for Evaluating Interstitial Lung Disease.

Ultrasound quarterly·2025
Same author

Derivation of a simple risk calculator for predicting clinical worsening in patients with pulmonary hypertension due to interstitial lung disease.

JHLT open·2025
Same author

Receiver design for the REACH global 21-cm signal experiment.

Experimental astronomy·2025

Related Experiment Video

Updated: Jun 22, 2025

A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
09:33

A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology

Published on: February 7, 2015

16.3K

A deep learning algorithm to identify carotid plaques and assess their stability.

Lan He1,2, Zekun Yang3, Yudong Wang3

  • 1Department of Ultrasound Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Frontiers in Artificial Intelligence
|July 2, 2024
PubMed
Summary

Deep learning models can accurately detect carotid plaques and assess their stability from ultrasound images, offering a more objective diagnostic tool for stroke risk assessment.

Keywords:
BCNN-ResNet algorithmscarotid plaque stabilityconvolutional neural networkdeep learningultrasound

More Related Videos

A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue
13:45

A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue

Published on: November 11, 2022

2.1K
A Model of Disturbed Flow-Induced Atherosclerosis in Mouse Carotid Artery by Partial Ligation and a Simple Method of RNA Isolation from Carotid Endothelium
11:00

A Model of Disturbed Flow-Induced Atherosclerosis in Mouse Carotid Artery by Partial Ligation and a Simple Method of RNA Isolation from Carotid Endothelium

Published on: June 22, 2010

28.5K

Related Experiment Videos

Last Updated: Jun 22, 2025

A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
09:33

A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology

Published on: February 7, 2015

16.3K
A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue
13:45

A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue

Published on: November 11, 2022

2.1K
A Model of Disturbed Flow-Induced Atherosclerosis in Mouse Carotid Artery by Partial Ligation and a Simple Method of RNA Isolation from Carotid Endothelium
11:00

A Model of Disturbed Flow-Induced Atherosclerosis in Mouse Carotid Artery by Partial Ligation and a Simple Method of RNA Isolation from Carotid Endothelium

Published on: June 22, 2010

28.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Disease

Background:

  • Carotid plaques are significant stroke risk factors.
  • Carotid ultrasound aids stroke risk assessment but can be subjective and time-consuming.
  • Deep learning offers potential for automated, objective carotid plaque analysis.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for detecting carotid plaques.
  • To assess the stability of carotid plaques using deep learning.
  • To provide a consistent and objective diagnostic method for carotid artery screening.

Main Methods:

  • Utilized a deep learning model fusing a bilinear convolutional neural network with a residual neural network (BCNN-ResNet).
  • Trained and tested the model on 3,860 ultrasound images from 1,339 participants (internal) and 1,564 images from 674 participants (external).
  • Evaluated model performance using Area Under the Curve (AUC), sensitivity, and specificity.

Main Results:

  • For plaque detection, the model achieved AUCs of 0.989 (internal) and 0.951 (external), with high sensitivity and specificity.
  • For plaque stability assessment, the model achieved AUCs of 0.896 (internal) and 0.854 (external).
  • The algorithm demonstrated strong performance in identifying both the presence and stability of carotid plaques.

Conclusions:

  • Deep learning algorithms, specifically BCNN-ResNet, show promise for routine ultrasound image analysis.
  • The developed model can effectively detect carotid plaques and assess their instability.
  • This automated approach can enhance objectivity and consistency in carotid plaque diagnosis.