Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.

ABSTRACT:

Background

Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored.

Methods

A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-r

SUBMITTER: Mayourian J 

PROVIDER: S-EPMC10948312 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature