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Unsupervised machine-learning identifies cardio-kidney-metabolic phenotypes and determines residual risk after acute coronary syndromes

29 August 2026 (12:45 - 13:00)
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Congress Presentation Part of: Young Investigator Award Session in Artificial Intelligence in Cardiology Secondary Prevention ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Miss Yifan Wang

University of Zurich, Zurich (Switzerland)

3 More presentations in this session

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Development and validation of a novel prediction model integrating AI-ECG and 0h high-sensitivity cardiac troponin T for immediate rule-out of acute myocardial infarction
Miss T. Tsao (Basel, CH)
Abstract
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End-to-end deep learning on coronary angiographic video predicts in-stent restenosis at dual peri-procedural time points
Doctor Z. Ba (Beijing, CN)
Abstract
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AI-empowered OCT is non-inferior to IVUS for media and plaque burden quantification: head-to-head histopathology validation using human coronary arteries
Doctor J. Huang (Shanghai, CN)
Abstract

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Young Investigator Award Session in Artificial Intelligence in Cardiology

The Event

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ESC Congress 2026

29 August 2026 12:45 CET
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