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Predicting hypertension risk using AI-derived retinal vascular complexity: a report from the Canadian Longitudinal Study on Aging (CLSA)

23 April 2026 (08:00 - 18:00)
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Congress Presentation Part of: Markers, sensors and algorithms for risk prediction Deep Learning Delegates Premium Access ESC Preventive Cardiology 2026 European Association of Preventive Cardiology (EAPC)

About the speaker

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Ms Simona Indzhova

McMaster University, Hamilton (Canada)

4 More presentations in this session

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Predicting healthcare costs in pulmonary arterial hypertension using WHO functional class and machine learning
Mr T. Kramer (Cologne, DE)
Abstract Slides
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The validity of smartphones and smartwatches to measure step metrics in free-living conditions
Ms I. Salzmann (Nijmegen, NL)
Abstract Slides
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Development of an integrated PHR-EHR alert system for near-term prevention of sudden cardiac death and cardiovascular events: a preliminary descriptive analysis from a prospective cohort study
Mr T. Noda (Kyoto, JP)
Abstract Slides
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Incorporating multimorbidity into cardiovascular competing risk models: performance in a national cohort
Doctor E. Church (Auckland, NZ)
Abstract Slides

Access the full session

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Markers, sensors and algorithms for risk prediction

The Event

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ESC Preventive Cardiology 2026

23 April - 25 April 2026
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ESC 365 is supported by

novartis Explore sponsored resources