ESC 365 - Longitudinal ECG trajectories identify high-risk aortic stenosis phenotypes years prior to TAVR

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Longitudinal ECG trajectories identify high-risk aortic stenosis phenotypes years prior to TAVR

Session: What can we learn from artificial intelligence-ECG interpretation?

Topic: Machine Learning

Speaker: Doctor M. Segar (Houston, US)

Event: ESC Congress 2025

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Doctor Matthew Segar

Texas Heart Institute, Houston (United States of America)

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4 more presentations in this session

Prospective validation of an ensemble deep learning algorithm for detecting structural heart disease using real-world 12-lead electrocardiogram images

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Development of a short-term mortality risk prediction model for patients with QTc prolongation based on 12-lead electrocardiogram

Speaker: Doctor C. Wang (Beijing, CN)

ECG-based artificial enhancement for risk stratification of thromboembolic and major adverse cardiac events in atrial fibrillation compared with CHA2DS2-VA scoring

Speaker: Associate Professor Y. Baek (Incheon, KR)

High-dimensional phenotypic matching with artificial intelligence-enhanced ECG to replicate heart failure trial outcomes in real-world data

Speaker: Doctor D. Biswas (New Haven, US)

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What can we learn from artificial intelligence-ECG interpretation?

Speakers: Doctor M. Segar, Doctor A. Poopak, Doctor C. Wang, Associate Professor Y. Baek, Doctor D. Biswas

About the event

ESC Congress 2025

29 August - 1 September 2025

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