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Unsupervised trajectory-based clustering identifies a rapid progression phenotype in patients with aortic stenosis

28 August 2026 (08:15 - 08:27)
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Congress Presentation Part of: Artificial intelligence-enabled echocardiography: phenotypes, prediction, and practice Valvular Heart Disease ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Doctor Junyi Zhang

National Cerebral and Cardiovascular Centre, Osaka (Japan)

4 More presentations in this session

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Clinical phenotypes and survival in multivalvular heart disease: differential prognostic impact of valve combinations
Doctor K. Barmpagiannos (Oklahoma City, US)
Abstract Slides Video
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Utility of multi-expert approach for validation of AI in echocardiography
Doctor S. Chawla (London, GB)
Abstract Slides Video
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Combining AI-enabled acquisition and interpretation for screening aortic stenosis: a prospective validation and pragmatic workflow
Doctor G. Tsaban (Rochester, US)
Abstract Slides Video
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AI-based diagnostic model for functional myocardial ischemia using resting echocardiographic strain analysis
Doctor Y. Liu (Beijing, CN)
Abstract Slides Video

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Artificial intelligence-enabled echocardiography: phenotypes, prediction, and practice
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The Event

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ESC Congress 2026

28 August 2026 08:15 CET
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