Related Experiment Video
Updated: Sep 2, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease
Pierre Elias1, Timothy J Poterucha1, Vijay Rajaram1
1Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Insights
Deep learning models can accurately detect valvular heart disease (VHD) from electrocardiograms (ECG). This technology shows promise for developing a VHD screening program to improve diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Valvular heart disease (VHD) significantly contributes to cardiovascular morbidity and mortality.
- VHD remains underdiagnosed, highlighting the need for improved detection methods.
- Electrocardiography (ECG) deep learning analysis shows potential for identifying specific VHDs like aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR).
Purpose of the Study:
- To develop and validate deep learning algorithms using ECG data.
- To accurately identify moderate to severe cases of AS, AR, and MR, individually and in combination.
- To assess the potential of these algorithms for a VHD screening program.
Main Methods:
- Utilized a large cohort of 77,163 patients with ECGs preceding echocardiography.
- Split data into training, validation, and testing sets for robust model development and evaluation.
- Assessed model performance using AU-ROC and precision-recall curves, including external validation and simulation of screening efficacy.
Main Results:
- Achieved high accuracy in detecting AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), and MR (AU-ROC: 0.83).
- The combined detection of any VHD showed an AU-ROC of 0.84 with 78% sensitivity and 73% specificity.
- External validation confirmed similar accuracy, and screening simulations demonstrated dependence on prevalence and sensitivity levels.
Conclusions:
- Deep learning analysis of ECGs can effectively detect AS, AR, and MR.
- This multicenter study provides a foundation for developing a VHD screening program.
- ECG-based AI holds potential for earlier and more widespread VHD detection.
Background:
Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR).
Objectives:
This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination.
Methods:
A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model.
Results:
The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively.
Conclusions:
Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.
Related Concept Videos
01:28Electrocardiogram Fundamentals
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...
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
01:24Cardiac Catheterization III: Left Heart Catheterization
01:17Imaging Studies for Cardiovascular System I:Echocardiography
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
01:25Correlation between ECG and 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...
01:22Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies