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External validation, robustness, and clinical error profiling of a multimodal electrocardiogram model for structural heart disease reveals a dominant shared prediction component

30 August 2026 (11:15 - 12:00)
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Congress Presentation Part of: New insights into artificial intelligence-enabled ECG: from foundation models to clinical validation Deep Learning ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Doctor Gabriel Riedemann

University of Gottingen, Goettingen (Germany)

5 More presentations in this session

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Language-aligned ECG foundation modeling for data-efficient detection of cardiovascular diseases
Assistant Professor M. Ko (La Jolla, US)
Abstract Slides
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Deep learning–based ECG detection and phenotype classification of hypertrophic cardiomyopathy
Assistant Professor M. SOH (Suwon, KR)
Abstract Slides
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Generative machine learning applied to the electrocardiogram (ECG) to uncover relevant ECG features in distinguishing takotsubo syndrome from myocardial infarction
Doctor H. Hakansson (Stockholm, SE)
Abstract Slides
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Multimodal ECG–language contrastive pretraining enables detection of structural heart disease from 12-Lead ECG
Doctor A. El-Medany (London, GB)
Abstract Slides
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Deep learning ECG detection of wall motion abnormalities suggesting silent coronary artery disease
Assistant Professor M. SOH (Suwon, KR)
Abstract Slides

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New insights into artificial intelligence-enabled ECG: from foundation models to clinical validation

The Event

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ESC Congress 2026

30 August 2026 11:15 CET
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