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

Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

36
Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
36
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

7.4K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
7.4K
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

12
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
12
Electrocardiogram01:29

Electrocardiogram

2.5K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
2.5K
Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies

38
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...
38
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

388
Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
388

You might also read

Related Articles

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

Sort by
Same author

Decoding the Complexity of Tricuspid Regurgitation Prognostic Drivers Using Explainable Artificial Intelligence.

European heart journal. Cardiovascular Imaging·2026
Same author

Improving structural heart disease screening: AI-ECG and novice AI-guided focused cardiac ultrasound.

NPJ digital medicine·2026
Same author

Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction.

European heart journal. Digital health·2026
Same author

Quantitative Analysis of the Impact of Region of Interest Information on Deep Learning Algorithms for Thyroid Ultrasound Imaging.

IEEE open journal of engineering in medicine and biology·2026
Same author

BRD2 upregulation as a pan-cancer adaptive resistance mechanism to BET inhibition.

Cellular & molecular biology letters·2026
Same author

First-in-Human Clinical Experience With Focal Pulsed Field and Radiofrequency Dual-Modality Ablation for Treatment Refractory Left Ventricular Summit PVCs.

Circulation. Arrhythmia and electrophysiology·2026

Related Experiment Video

Updated: Jul 28, 2025

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
07:41

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

3.8K

Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic

Saki Ito1, Michal Cohen-Shelly1,2, Zachi I Attia1

  • 1Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW Rochester, MN 55905, USA.

European Heart Journal. Digital Health
|June 2, 2023
PubMed
Summary

Artificial intelligence-enabled electrocardiogram (AI-ECG) can detect aortic stenosis (AS) by reflecting AS severity, diastolic dysfunction, and left ventricular hypertrophy. This AI-ECG model identifies AS multifactorially, offering a promising tool for early detection.

Keywords:
AIAortic stenosisConvolutional neural networkECG

More Related Videos

Ultrasonic Assessment of Myocardial Microstructure
10:53

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

5.5K
High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart
11:50

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

Published on: July 9, 2010

24.2K

Related Experiment Videos

Last Updated: Jul 28, 2025

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
07:41

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

3.8K
Ultrasonic Assessment of Myocardial Microstructure
10:53

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

5.5K
High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart
11:50

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

Published on: July 9, 2010

24.2K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Artificial intelligence-enabled electrocardiogram (AI-ECG) shows promise for detecting aortic stenosis (AS) before symptom onset.
  • The specific cardiac functional, structural, or hemodynamic components influencing AI-ECG detection of AS remain unclear.

Purpose of the Study:

  • To investigate the correlation between AI-ECG probability of AS and echocardiographic parameters in a large patient cohort.
  • To identify the underlying cardiac features reflected by AI-ECG in AS detection.

Main Methods:

  • Applied a convolutional neural network-based AI-ECG model developed at Mayo Clinic to identify moderate-to-severe AS.
  • Analyzed correlations between AI-ECG probability and echocardiographic measurements in 102,926 patients.
  • Examined associations with patient demographics and comorbidities.

Main Results:

  • AI-ECG identified 27.7% of patients as AS positive.
  • AI-ECG correlated significantly with aortic valve area, peak velocity, mean pressure gradient, left ventricular mass index, E/e', and left atrium volume index.
  • Age showed a strong correlation with AI-ECG, similar to its correlation with echocardiographic parameters.

Conclusions:

  • AI-ECG detection of AS is multifactorial, reflecting a combination of AS severity, diastolic dysfunction, and left ventricular hypertrophy.
  • The AI-ECG model appears to capture a graded representation of cardiac anatomical and functional features for AS identification.