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CORRECTION article

Front. Neurol., 09 May 2022
Sec. Stroke
Volume 13 - 2022 | https://doi.org/10.3389/fneur.2022.921992

Corrigendum: Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability

Yang Wang1 Junkai Zhu2 Jinli Zhao3 Wenyi Li4 Xin Zhang5 Xiaolin Meng6 Taige Chen7 Ming Li1 Meiping Ye1 Renfang Hu8 Shidan Dou6 Huayin Hao6 Xiaofen Zhao9 Xiaoming Wu9 Wei Hu10 Cheng Li6 Xiaole Fan11 Liyun Jiang2 Xiaofan Lu2* Fangrong Yan2*
  • 1Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
  • 2State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
  • 3Department of Radiology, The Affiliated Hospital of Nantong University, Nantong, China
  • 4Department of Endocrinology, Tongren Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
  • 5Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
  • 6Research & Advanced Algorithm Department of HSW BU, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
  • 7Medical School of Nanjing University, Nanjing, China
  • 8Calibration Physical Algorithm Department of CT BU, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
  • 9Clinical Workflow and Clinical Verification Department of CT BU, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
  • 10Department of CT BU, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
  • 11Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China

A Corrigendum on
Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability

by Wang, Y., Zhu, J., Zhao, J., Li, W., Zhang, X., Meng, X., Chen, T., Li, M., Ye, M., Hu, R., Dou, S., Hao, H., Zhao, X., Wu, X., Hu, W., Li, C., Fan, X., Jiang, L., Lu, X., and Yan, F. (2022). Front. Neurol. 13:755492. doi: 10.3389/fneur.2022.755492

In the published article, there was an error in affiliation “1.” Instead of “Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China,” it should be “Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.”

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: stroke, deep learning, computed tomography, automatic cranial scanning, accurate and repeatable images

Citation: Wang Y, Zhu J, Zhao J, Li W, Zhang X, Meng X, Chen T, Li M, Ye M, Hu R, Dou S, Hao H, Zhao X, Wu X, Hu W, Li C, Fan X, Jiang L, Lu X and Yan F (2022) Corrigendum: Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability. Front. Neurol. 13:921992. doi: 10.3389/fneur.2022.921992

Received: 17 April 2022; Accepted: 19 April 2022;
Published: 09 May 2022.

Approved by: Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2022 Wang, Zhu, Zhao, Li, Zhang, Meng, Chen, Li, Ye, Hu, Dou, Hao, Zhao, Wu, Hu, Li, Fan, Jiang, Lu and Yan. 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: Xiaofan Lu, xlu.cpu@foxmail.com; Fangrong Yan, f.r.yan@outlook.com

These authors have contributed equally to this work

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