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

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

24
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
24
Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies

43
The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
43
Heart Valves01:16

Heart Valves

5.3K
The human heart is a complex organ with an intricate system of valves that regulate blood flow. There are two main types of valves: atrioventricular (AV) valves and semilunar valves.
The AV valves prevent the backflow of blood from the ventricles to the atria during ventricular contraction. These valves function with the assistance of the chordae tendineae and papillary muscles. When the ventricles are relaxed, the chordae tendineae are slack, allowing blood to flow from the atria into the...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Analysis of Vascular Mechanical Characteristics after Coronary Degradable Stent Implantation.

BioMed research international·2020
See all related articles

Related Experiment Video

Updated: Aug 12, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

809

A Computer-Aided Heart Valve Disease Diagnosis System Based on Machine Learning.

Si-Ji Ding1, Hao Ding2, Meng-Fei Kan1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Journal of Healthcare Engineering
|February 2, 2023
PubMed
Summary

A new computer-aided system for heart valve disease diagnosis uses artificial intelligence to analyze heart sounds, achieving 97.5% accuracy. This technology assists doctors, especially in remote areas, for earlier and more reliable detection of heart conditions.

More Related Videos

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.7K
Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement
09:57

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement

Published on: January 20, 2022

2.7K

Related Experiment Videos

Last Updated: Aug 12, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

809
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.7K
Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement
09:57

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement

Published on: January 20, 2022

2.7K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiac auscultation is a vital but experience-dependent method for diagnosing heart valve disease.
  • Shortages of experienced cardiologists, particularly in remote regions, limit accurate early diagnosis.
  • A reliable, automated system is needed to support clinical decision-making.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis system for heart valve disease.
  • To improve the accuracy and accessibility of heart valve disease diagnosis.
  • To assist healthcare professionals in identifying common heart valve conditions.

Main Methods:

  • Collected a dataset of five heart sound categories (normal, mitral stenosis, mitral regurgitation, aortic stenosis).
  • Trained diagnostic models using a modified GoogLeNet convolutional neural network and weighted K-Nearest Neighbors (KNN).
  • Engineered software for heart sound acquisition, visualization, and AI-driven diagnosis.

Main Results:

  • The modified GoogLeNet model achieved an overall accuracy of 97.5% for diagnosing four types of heart valve diseases.
  • Average diagnostic metrics included 98.75% accuracy, 96.88% sensitivity, 99.22% specificity, and 97.99% F1 score.
  • The system successfully visualized heart sound waveforms and provided diagnostic suggestions.

Conclusions:

  • The developed computer-aided diagnosis system demonstrates high accuracy in identifying heart valve diseases.
  • The system offers a valuable tool to augment cardiologist expertise, particularly in underserved areas.
  • This technology has the potential to enhance early detection and management of valvular heart conditions.