Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.

ABSTRACT:

Background and aims

Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS).

Methods

Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the

SUBMITTER: Jabbour G 

PROVIDER: S-EPMC11631091 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature