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CORRECTION article

Front. Cardiovasc. Med., 13 June 2022
Sec. Cardiovascular Imaging
Volume 9 - 2022 | https://doi.org/10.3389/fcvm.2022.920738

Corrigendum: Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

This article is a correction to:

  1. Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

    1. Read original article
\nLixue Xu&#x;Lixue Xu1†Yi He&#x;Yi He1†Nan LuoNan Luo1Ning GuoNing Guo2Min HongMin Hong3Xibin JiaXibin Jia4Zhenchang Wang Zhenchang Wang1*Zhenghan Yang Zhenghan Yang1*
  • 1Affiliated Beijing Friendship Hospital, Capital Medical University, Beijing, China
  • 2Shukun (Beijing) Technology Co., Ltd., Beijing, China
  • 3Department of Computer Software Engineering, Soonchunhyang University, Asan-si, South Korea
  • 4Faculty of Information Technology, Beijing University of Technology, Beijing, China

A Corrigendum on
Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

by Xu, L., He, Y., Luo, N., Guo, N., Hong, M., Jia, X., Wang, Z., and Yang, Z. (2021). Front. Cardiovasc. Med. 8:707508. doi: 10.3389/fcvm.2021.707508

In the original article, we neglected to include the funder “National Key Research and Development Program of China (2019YFE0107800), Beijing Municipal Science and Technology Commission (Z201100005620009) to ZY, and National Research Foundation of Korea (2019K1A3A1A20093097) to MH.”

The correct funding statement appears below:

“This study received funding from National Key Research and Development Program of China (2019YFE0107800), Beijing Municipal Science and Technology Commission (Z201100005620009) to ZY, and National Research Foundation of Korea (2019K1A3A1A20093097) to MH. The funders had the following involvement with the study. All the funders provided financial support for patient enrollment, data collection, database construction, and management.”

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: coronary artery disease, computed tomographic angiography, deep learning, invasive coronary angiography (ICA), diagnostic test

Citation: Xu L, He Y, Luo N, Guo N, Hong M, Jia X, Wang Z and Yang Z (2022) Corrigendum: Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study. Front. Cardiovasc. Med. 9:920738. doi: 10.3389/fcvm.2022.920738

Received: 15 April 2022; Accepted: 24 May 2022;
Published: 13 June 2022.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2022 Xu, He, Luo, Guo, Hong, Jia, Wang and Yang. 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: Zhenghan Yang, yangzhenghan@vip.163.com; Zhenchang Wang, cjr.wzhch@vip.163.com

†These authors have contributed equally to this work

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.