📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation. (2021)

First Author: Corrado C
Attributed to:  Predicting Atrial Fibrillation Mechanisms Through Deep Learning funded by MRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1093/europace/euaa386

PubMed Identifier: 33437987

Publication URI: http://europepmc.org/abstract/MED/33437987

Type: Journal Article/Review

Volume: 23

Parent Publication: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

Issue: 23 Suppl 1

ISSN: 1099-5129