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CORRECTION article

Front. Physiol., 09 November 2021
Sec. Computational Physiology and Medicine
This article is part of the Research Topic Artificial Intelligence in Heart Modelling View all 24 articles

Corrigendum: Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation

  • 1School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
  • 2Department of Biomedical Engineering, ETHZürich, Zurich, Switzerland
  • 3Department of Computer Science, University of Oxford, Oxford, United Kingdom

A Corrigendum on
Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation

by Muizniece, L., Bertagnoli, A., Qureshi, A., Zeidan, A., Roy, A., Muffoletto, M., and Aslanidi, O. (2021). Front. Physiol. 12:733139. doi: 10.3389/fphys.2021.733139

There is an error in the Funding statement. The correct number for **British Heart Foundation** is **PG/15/8/31130**.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: atrial fibrillation, catheter ablation, patient imaging, reinforcement learning, deep learning

Citation: Muizniece L, Bertagnoli A, Qureshi A, Zeidan A, Roy A, Muffoletto M and Aslanidi O (2021) Corrigendum: Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation. Front. Physiol. 12:799585. doi: 10.3389/fphys.2021.799585

Received: 21 October 2021; Accepted: 22 October 2021;
Published: 09 November 2021.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2021 Muizniece, Bertagnoli, Qureshi, Zeidan, Roy, Muffoletto and Aslanidi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Oleg Aslanidi, oleg.aslanidi@kcl.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.