ArticleEuropean heart journal. Digital health2026
Deep learning analysis of single-lead electrocardiograms enables pragmatic heart failure risk assessment in the general population.
Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aims: Effective heart failure (HF) prevention requires early identification of high-risk individuals, yet population-wide stratification remains difficult. We evaluated whether deep learning using single-lead (lead I) electrocardiograms (ECGs), obtainable from medical systems and wearables, enables population-scale risk assessment. We developed AI-HF to estimate incident clinical HF risk using UK Biobank (UKB) data, validating in the prospective SHIP-START and SHIP-TREND cohorts. Methods and results: The analysis included 31 740 UKB participants (median age 64, 5.2 year follow-up, 243 events), 3025 SHIP-START participants (age 50, 15 year follow-up, 166 events), and 1342 SHIP-TREND participants (age 51, 9 year follow-up, 84 events). Participants with prevalent HF were excluded. Performance was evaluated at a harmonized 5-year prediction horizon. C-indices for incident clinical HF were 0.693 [95% confidence interval (CI) 0.654-0.732] in UKB, 0.715 (0.652-0.777) in SHIP-START, and 0.791 (0.749-0.833) in SHIP-TREND. Hazard ratios per standard deviation increase in AI-HF output were 1.67 (1.56-1.79), 1.43 (1.25-1.65), and 1.46 (1.34-1.59), respectively (all Conclusion: Across cohorts, AI-HF identified individuals at elevated 5-year incident clinical HF risk using single-lead ECGs. Given the ubiquity of wearables, this method may enable population-scale assessment to support targeted prevention and early intervention.
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