ArticleEuropean heart journal. Digital health2026
Deep learning algorithm for detection of acute heart failure using standard ECG waveforms.
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. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A bibliometric analysis of artificial intelligence applications in heart failure.Journal of thoracic disease · 2026Article
- External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.NPJ digital medicine · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: To develop and evaluate a deep learning model for immediate and accurate diagnosis of acute heart failure(HF) using standard 12-lead electrocardiogram(ECG) waveforms collected from a large cohort of patients. Methods and results: We retrospectively analysed patients aged > 18 years who underwent transthoracic echocardiogram, Conclusion: The proposed deep learning models demonstrated remarkable performance in diagnosing acute HF. These findings support its potential utility in facilitating early diagnosis and improving clinical outcomes.
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Registered trials
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