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Explainable and interpretable AI visualises self-learned clinically relevant ECG characteristics of rhythm and morphology paving the way for trustworthy diagnostic support

1 September 2025 (10:15 - 11:00)
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Congress Presentation Part of: Smart ECGs: bridging technology and cardiovascular care Deep Learning ESC Professional Premium Access ESC Congress 2025 European Society of Cardiology

About the speaker

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Mr Alexander Hammer

Dresden University Of Technology, Dresden (Germany)

6 More presentations in this session

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AI-ECG performance in detecting left ventricular systolic dysfunction and predicting death in Chagas disease patients: SaMi-Trop project
Associate Professor C. Cardoso (Divinopolis, BR)
Abstract Slides Video
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AI-Enhanced ECG for pulmonary hypertension detection: integrating machine learning and deep learning approaches
Doctor J. Huang (Taipei, TW)
Abstract Slides Video
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AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients
Doctor D. Pavluk (Innsbruck, AT)
Abstract Slides Video
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Prospective validation in a real-world cohort of a deep learning model for left ventricular filling pressure estimation using standard 12-lead ECG
Doctor A. Cinq-Mars (Paris, FR)
Abstract Slides Video
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AI-driven analysis of treadmill test ECG: clinical utility and performance evaluation
Mr Y. Na (Seoul, KR)
Abstract Slides Video

Access the full session

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Smart ECGs: bridging technology and cardiovascular care

The Event

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ESC Congress 2025

1 September 2025 10:15 CET
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novartis Explore sponsored resources