Electrocardiography-based artificial intelligence predicts the upcoming future of heart failure with mildly reduced ejection fraction.

ABSTRACT:

Background

Heart failure with mildly reduced ejection fraction (HFmrEF) has emerged as the predominant subtype of heart failure (HF). This study aimed to develop artificial intelligence (AI)-electrocardiography (ECG) to identify and predict the prognosis of patients with HFmrEF.

Methods

We collected 104,336 12-lead ECG datasets from April 2009 to December 2021 in a tertiary centre. The AI-ECG encompasses a novel model that combines an automatic labelling preprocessing method with a transformer architecture incorporating a triplet loss for HFmrEF analysis.

Results

The receiver operating characteristic analyses revealed that the area under the curve of AI-ECG for identifying all types of HF was acceptable [0.873, 95% confidence interval (CI): 0.864-0.893], while that fo

SUBMITTER: Kim DY 

PROVIDER: S-EPMC11847893 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature