ArticleFrontiers in cardiovascular medicine2025
Electrocardiography-based artificial intelligence predicts the upcoming future of heart failure with mildly reduced ejection fraction.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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9 authors.
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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 for identifying patients with HFmrEF was relatively lower (0.824, 95% CI: 0.794-0.863) than that for those with HF with reduced ejection fraction (EF) (0.875, 95% CI: 0.844-0.912) and those with normal EF (0.870, 95% CI: 0.842-0.894). The analysis of ECG features showed significant increases in QRS duration ( Conclusions: AI-ECG presents an innovative approach for the prognostic stratification of cardiac contractility in patients with HFmrEF. In patients with HFmrEF, disease progression can be predicted using AI-ECG.
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