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

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

706
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
706
Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

25
Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
25

You might also read

Related Articles

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

Sort by
Same author

Prognostic impact of platelet count trajectories in acute decompensated heart failure: a retrospective cohort analysis.

BMC cardiovascular disorders·2026
Same author

Learning protein representations with conformational dynamics.

Bioinformatics (Oxford, England)·2026
Same author

Association of polycythemia with outcomes of acute decompensated heart failure: A matched and weighted cohort analysis.

PloS one·2026
Same author

Normoferritinemic Versus Hyperferritinemic Inflammation in Patients Admitted to the Department of Internal Medicine.

Journal of clinical medicine·2026
Same author

Angiographic and functional assessment after paclitaxel or sirolimus drug-coated balloons for de novo coronary artery disease in small vessels: PICCOLETO VI study.

AsiaIntervention·2026
Same author

Revascularization of Left Anterior Descending Artery with Minimally Invasive Direct Coronary Artery Bypass Graft vs. Drug Eluting Stents: A Retrospective, Two-Center Study.

Journal of clinical medicine·2026
Same journal

Evidence-Based Clinical Recommendations for the Appropriate Use of Diagnostic Tests in Pediatric Allergology: Focus on Asthma, Rhinoconjunctivitis, and Keratoconjunctivitis Vernal.

Journal of clinical medicine·2026
Same journal

Surgical and Transcatheter Approach of a Failed Mitral Valve Repair: A Comprehensive Review on Selecting the Most Suitable Approach.

Journal of clinical medicine·2026
Same journal

Hybrid Metaheuristic Feature Selection for Breast Cancer Detection in Digital Mammography: A Feasibility Study with Nested Validation, Benchmarking, and External Stress Testing.

Journal of clinical medicine·2026
Same journal

Identity Transformation and the Role of Accountability in Recovery from Problematic Pornography Use: A Phenomenological-Hermeneutical Study.

Journal of clinical medicine·2026
Same journal

Does Early Surgical Treatment in Degenerative Cervical Myelopathy Have a Favorable Clinical Outcome and Impact on Quality of Life?

Journal of clinical medicine·2026
Same journal

Shear Wave Elastography in Musculoskeletal Imaging: A Narrative Review.

Journal of clinical medicine·2026
See all related articles

Related Experiment Video

Updated: Aug 19, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

4.1K

Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a

Tomer Golany1, Kira Radinsky1, Natalia Kofman2,3

  • 1Taub Faculty of Computer Sciences, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Journal of Clinical Medicine
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms (MLA) show promise in detecting left ventricular systolic dysfunction (LVSD) from ECGs, outperforming physicians. This technology could aid early detection and improve patient outcomes.

Keywords:
artificial intelligenceearly diagnosiselectrocardiogramheart failuremachine learning

More Related Videos

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

853
Ultrasonic Assessment of Myocardial Microstructure
10:53

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

5.5K

Related Experiment Videos

Last Updated: Aug 19, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

4.1K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

853
Ultrasonic Assessment of Myocardial Microstructure
10:53

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

5.5K

Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Early detection of left ventricular systolic dysfunction (LVSD) is crucial for timely intervention and improved patient outcomes, particularly in asymptomatic individuals.
  • Standard 12-lead electrocardiography (ECG) presents a potential non-invasive method for predicting LVSD.
  • Physician interpretation of ECGs for LVSD can be subjective, highlighting the need for objective assessment tools.

Purpose of the Study:

  • To compare the diagnostic performance of Machine Learning Algorithms (MLA) against human physicians in predicting LVSD using standard 12-lead ECG data.
  • To evaluate the efficacy of a deep residual convolutional neural network in identifying LVSD based on ECG.
  • To assess the impact of different ejection fraction (EF) thresholds on diagnostic accuracy.

Main Methods:

  • A deep residual convolutional neural network was developed and trained on a large dataset of 13,820 ECG and echocardiography pairs.
  • The trained MLA and six independent physicians assessed a separate test set of 850 ECGs for the presence of LVSD (defined as EF < 50% and EF < 35%).
  • Performance was quantified using sensitivity, specificity, and C-statistics, with interobserver agreement assessed among physicians.

Main Results:

  • The MLA achieved a C-statistic of 0.85 for LVSD (EF < 50%), significantly outperforming the average physician sensitivity (70%) and specificity (70%) with moderate interobserver agreement (κ = 0.50).
  • When LVSD was defined as EF < 35%, physician sensitivity improved to 84% but specificity dropped to 57%.
  • The MLA demonstrated improved performance with a C-statistic of 0.88 for the stricter LVSD definition (EF < 35%).

Conclusions:

  • Machine learning algorithms demonstrate superior performance compared to physicians in predicting LVSD from standard 12-lead ECGs.
  • While MLA shows significant potential for LVSD screening, further validation and integration into clinical workflows are necessary.
  • Physicians can consider utilizing MLA-based predictions as a supplementary tool for LVSD screening to enhance diagnostic accuracy and patient care.