Deep learning to predict left ventricular hypertrophy from the electrocardiogram.

ABSTRACT:

Aims

Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using electrocardiogram (ECG) and clinical variables in 37 534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP).

Methods and results

We analysed 12-lead ECGs of 48 835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%), and test set (15%) for performan

SUBMITTER: Naderi H 

PROVIDER: S-EPMC12933001 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature