Neural network-derived electrocardiographic features have prognostic significance and important phenotypic and genotypic associations

25 August 2023 (07:30 - 18:30)

Organised by:

Sign in for access options

Sign in to view the resources

Sign in for access options

About the speaker

Doctor Libor Pastika

Imperial College London, London (United Kingdom of Great Britain & Northern Ireland)

4 More presentations in this session

Presentation thumbnail

State-of-the-art lecture on artificial intelligence in cardiology

Professor U. Landmesser (Berlin, DE)

Presentation thumbnail

Artificial intelligence-derived ECG bio-index as a tool for predicting the prognosis of patients with ST-segment elevation myocardial infarction underwent primary percutaneous coronary intervention

Assistant Professor K. Jeon (Seongnam, KR)

Abstract Slides Video

Presentation thumbnail

Validation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standard

Doctor E. Ronner (Delft, NL)

Abstract Slides Video

Presentation thumbnail

Reducing the burden of inconclusive smart-device single-lead ECG tracings via a novel atificial intelligence algorithm

Mr S. Weidlich (Basel, CH)

Abstract Slides Video

Access the full session

Session thumbnail

The latest in cardiovascular diagnostics supported by artificial intelligence

The Event

Event poster

ESC Congress 2023

25 August - 28 August 2023

You may be interested in

Congress Presentation

Electrocardiogram-based artificial intelligence to identify prevalent coronary artery disease

Congress Presentation

Leveraging deep learning for detecting atrial fibrillation and flutter and predicting thromboembolic events from images of 12-lead electrocardiograms in sinus rhythm

Congress Session

Artificial intelligence in arrhythmia care: good news for EPeeps?

ESC 365 is supported by

novartis

Explore sponsored resources

ESC 365 is supported by

novartis

Explore sponsored resources