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Machine-learning integration of complex echocardiographic patterns and clinical parameters from cohorts and trials

31 August 2019 (00:00 - 00:00)
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Congress Presentation Part of: Novel echocardiography techniques for heart failure Epidemiology, Prognosis, Outcome ESC Premium Access ESC Congress 2019

About the speaker

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Professor Maja Cikes

University Hospital Centre Zagreb, Zagreb (Croatia)

7 More presentations in this session

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Strain parameters at rest and after exercise in symptomatically stable patients with improved heart failure - STRESS-HF
Doctor M. Saran (Lucknow, IN)
Abstract Slides Video
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Diastolic dysfunction is an independent predictor of adverse events in patients with systolic dysfunction. Insights from the MADIT-CRT trial
Doctor M. Gori (Bergamo, IT)
Abstract Slides Video
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Revisiting the weber and ventilatory classifications in heart failure by combining exercise stress echo with gas exchange phenotyping
Doctor T. Sugimoto (Nagoya, JP)
Abstract Slides Video
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Fast-SENC quantifies segmental right ventricular intramyocardial strain to assess subclinical RV dysfunction prior to changes in RV ejection fraction
Doctor M. Montenbruck (Hamburg, DE)
Abstract
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The interrelation of left ventricular geometry with myocardial work
Doctor F. Sahiti (Wurzburg, DE)
Abstract

Access the full session

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Novel echocardiography techniques for heart failure

The Event

Event poster

ESC Congress 2019

31 August - 4 September 2019

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources