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.
Summary
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
Volume
12 - 2021
Updates
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
This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology
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.