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Brugada syndrome: a multicenter study on ECG image-based self-supervised learning for non-invasive prediction of sodium channel blocker response

28 August 2026 (10:15 - 11:00)
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Congress Presentation Part of: Deep learning for prediction and diagnosis Electrocardiogram (ECG) and Arrhythmia Analysis ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Doctor Nawar Alhourani

University Hospital Munster - UKM, Muenster (Germany)

6 More presentations in this session

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Deep learning for sudden cardiac arrest risk prediction: a meta-analysis of discriminative performance
Doctor D. Koeckerling (Heidelberg, DE)
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Comparative evaluation of feature-based, deep learning, and foundation models for ECG-based detection of left heart dilatation and hypertrophy
Doctor K. Ng (Taichung, TW)
Abstract Slides
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An ECG-based interpretable machine learning approach for the differential diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy
Doctor A. Cecconi (Madrid, ES)
Abstract Slides
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Electrocardiogram-based deep learning model for prediction of ventricular tachycardia in high-risk patients with hypertrophic cardiomyopathy.
Ms M. Al-Adawi (Muscat, OM)
Abstract Slides
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Deep learning estimation of LVEF and GLS from echocardiographic videos to determine cancer therapy-related cardiac dysfunction
Doctor E. Guerra (Mexico City, MX)
Abstract Slides

Access the full session

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Deep learning for prediction and diagnosis

The Event

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ESC Congress 2026

28 August 2026 10:15 CET
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novartis Explore sponsored resources
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