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Updated: Aug 8, 2026
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult
Joshua Mayourian1, Ivor B Asztalos2, Amr El-Bokl1
1Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
Insights
Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis accurately predicts left ventricular systolic dysfunction (LVSD) in children with congenital heart disease. This tool aids in identifying current and future risks, improving cardiovascular health management.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Left ventricular systolic dysfunction (LVSD) is a significant risk factor for cardiovascular events in patients with congenital heart disease (CHD).
- Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows potential for predicting LVSD in adults, but its application in diverse CHD populations is limited.
Purpose of the Study:
- To develop and validate an AI-ECG algorithm for detecting LVSD in patients with various congenital heart disease lesions across all ages.
- To assess the algorithm's ability to predict future cardiac dysfunction and mortality risk in this population.
Main Methods:
- A convolutional neural network was trained on paired ECG-echocardiograms from a large cohort of CHD patients.
- Model performance was evaluated using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves in internal and external validation datasets.
Main Results:
- The AI-ECG algorithm demonstrated high performance in detecting LVSD (internal AUROC 0.95, external AUROC 0.96).
- Patients identified as high-risk by AI-ECG showed a significantly increased likelihood of future dysfunction (HR 12.1) and mortality.
- Key ECG features associated with high-risk included precordial QRS complexes, T waves, deep V2 S waves, and lateral precordial T wave inversion.
Conclusions:
- The externally validated AI-ECG algorithm is a promising tool for predicting current and future LVSD in patients with CHD.
- This technology offers a clinically impactful, cost-effective, and accessible method for cardiovascular health monitoring in the CHD population.
Background:
Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions.
Methods:
We trained a convolutional neural network on paired ECG-echocardiograms (≤2 days apart) across the lifespan of a wide range of congenital heart disease lesions to detect left ventricular ejection fraction (LVEF) of 40% or less. Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital (Boston, MA, USA) and externally at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves.
Findings:
The training cohort comprised 124 265 ECG-echocardiogram pairs (49 158 patients; median age 10·5 years [IQR 3·5-16·8]; 3381 [2·7%] of 124 265 ECG-echocardiogram pairs with LVEF ≤40%). Test groups included internal testing (21 068 patients; median age 10·9 years [IQR 3·7-17·0]; 3381 [2·7%] of 124 265 ECG-echocardiogram pairs with LVEF ≤40%) and external validation (42 984 patients; median age 10·8 years [IQR 4·9-15·0]; 1313 [1·7%] of 76 400 ECG-echocardiogram pairs with LVEF ≤40%) cohorts. High model performance was achieved during internal testing (AUROC 0·95, AUPRC 0·33) and external validation (AUROC 0·96, AUPRC 0·25) for a wide range of congenital heart disease lesions. Patients with LVEF greater than 40% by echocardiogram who were deemed high risk by AI-ECG were more likely to have future dysfunction compared with low-risk patients (hazard ratio 12·1 [95% CI 8·4-17·3]; p<0·0001). High-risk patients by AI-ECG were at increased risk of mortality in the overall cohort and lesion-specific subgroups. Common salient features highlighted across congenital heart disaese lesions include precordial QRS complexes and T waves, with common high-risk ECG features including deep V2 S waves and lateral precordial T wave inversion. A case study on patients with ventricular pacing showed similar findings.
Interpretation:
Our externally validated algorithm shows promise in prediction of current and future LVSD in patients with congenital heart disease, providing a clinically impactful, inexpensive, and convenient cardiovascular health tool in this population.
Funding:
Kostin Innovation Fund, Thrasher Research Fund Early Career Award, Boston Children's Hospital Electrophysiology Research Education Fund, National Institutes of Health, National Institute of Childhood Diseases and Human Development, and National Library of Medicine.
