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Diagnostic performance of traditional troponin-based predictive models and explainable ML-models in patients with suspected AMI

30 August 2026 (11:15 - 12:00)
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Congress Presentation Part of: Machine learning in acute coronary syndromes Machine Learning ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Mrs Diona Gjermeni

Stanford Center for Clinical Research, Stanford (United States of America)

5 More presentations in this session

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False-negative constrained machine learning for coronary angiography referral in vascular patients
Doctor J. Petrovic (Belgrade, RS)
Abstract
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A fully automated deep learning framework for assessing left ventricular systolic function from coronary angiography with validation in acute myocardial infarction
Doctor S. Chang (Taichung, TW)
Abstract
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Early prediction of low cardiac output syndrome in post-cardiotomy patients using supervised machine learning
Doctor J. Garate Togo (Mexico City, MX)
Abstract
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Robust cross center coronary artery segmentation on computed tomography angiography with limited local annotations
Miss L. Alinezhad (Bologna, IT)
Abstract
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A proof-of-concept machine-learning model improves pre-test probability estimation for obstructive coronary artery disease: benchmarking against the ESC guidelines
Mr D. Belcak (Hradec Kralove, CZ)
Abstract

Access the full session

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Machine learning in acute coronary syndromes

The Event

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ESC Congress 2026

30 August 2026 11:15 CET
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