Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response.

ABSTRACT:

Aims

Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting LV systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR).

Methods and results

This study is an external validation of a pre-existing deep learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤ 40%, assessed using 12-lead electrocardiography (ECG). Secon

SUBMITTER: Jeong JH 

PROVIDER: S-EPMC11570393 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature