ESC 365 - AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients

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AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients

Session: Smart ECGs: bridging technology and cardiovascular care

Topic: Deep Learning

Speaker: Doctor D. Pavluk (Innsbruck, AT)

Event: ESC Congress 2025

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Doctor Daniel Pavluk

Medical University of Innsbruck, Innsbruck (Austria)

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6 more presentations in this session

AI-ECG performance in detecting left ventricular systolic dysfunction and predicting death in Chagas disease patients: SaMi-Trop project

Speaker: Associate Professor C. Cardoso (Divinopolis, BR)

AI-Enhanced ECG for pulmonary hypertension detection: integrating machine learning and deep learning approaches

Speaker: Doctor J. Huang (Taipei, TW)

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

Speaker: Mr A. Hammer (Dresden, DE)

Prospective validation in a real-world cohort of a deep learning model for left ventricular filling pressure estimation using standard 12-lead ECG

Speaker: Doctor A. Cinq-Mars (Paris, FR)

AI-driven analysis of treadmill test ECG: clinical utility and performance evaluation

Speaker: Mr Y. Na (Seoul, KR)

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Smart ECGs: bridging technology and cardiovascular care

Speakers: Doctor D. Pavluk, Associate Professor C. Cardoso, Doctor J. Huang, Mr A. Hammer, Doctor A. Cinq-Mars...

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ESC Congress 2025

29 August - 1 September 2025

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