Front. Cardiovasc. Med. Frontiers in Cardiovascular Medicine Front. Cardiovasc. Med. 2297-055X Frontiers Media S.A. 10.3389/fcvm.2022.1078223 Cardiovascular Medicine Correction Corrigendum: Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care Chen Ke-Wei 1 2 † Wang Yu-Chen 1 3 4 † Liu Meng-Hsuan 5 Tsai Being-Yuah 5 Wu Mei-Yao 6 7 Hsieh Po-Hsin 8 Wei Jung-Ting 1 9 Shih Edward S. C. 10 Shiao Yi-Tzone 11 Hwang Ming-Jing 10 Wu Ya-Lun 5 Hsu Kai-Cheng 5 Chang Kuan-Cheng 1 9 * 1Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan 2Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan 3Division of Cardiovascular Medicine, Asia University Hospital, Taichung, Taiwan 4Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, Taiwan 5AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan 6School of Post-Baccalaureate Chinese Medicine, China Medical University, Taichung, Taiwan 7Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan 8Ever Fortune AI Co., Ltd., Taichung, Taiwan 9School of Medicine, China Medical University, Taichung, Taiwan 10Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan 11Center of Institutional Research and Development, Asia University, Taichung, Taiwan

Edited and reviewed by: Roohallah Alizadehsani, Deakin University, Australia

 *Correspondence: Kuan-Cheng Chang kuancheng.chang@gmail.com

This article was submitted to Cardiac Rhythmology, a section of the journal Frontiers in Cardiovascular Medicine

†These authors have contributed equally to this work

 09 12 2022 2022 9 1078223 24 10 2022 24 11 2022 Copyright © 2022 Chen, Wang, Liu, Tsai, Wu, Hsieh, Wei, Shih, Shiao, Hwang, Wu, Hsu and Chang. 2022 Chen, Wang, Liu, Tsai, Wu, Hsieh, Wei, Shih, Shiao, Hwang, Wu, Hsu and Chang

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.

 A corrigendum on Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care by Chen, K.-W., Wang, Y.-C., Liu, M.-H., Tsai, B.-Y., Wu, M.-Y., Hsieh,P.-H., Wei, J.-T., Shih, E. S. C., Shiao, Y.-T., Hwang, M.-J., Wu Y.-L., Hsu, K.-C., and Chang, K.-C. (2022). Front. Cardiovasc. Med. 9:1001982. doi: 10.3389/fcvm.2022.1001982 artificial intelligence (AI) contact-to-balloon (C2B) time convolutional neural network and long short-term memory (CNN-LSTM) prehospital 12-lead ECGs ST-elevation myocardial infarction (STEMI)

In the original article, there was an error in Figure 1: “The flowchart of the AI-based pre-hospital STEMI detection system” as published. A typo error in the figure read “Prehospital 12-lead ECG examinectin,” and has been corrected to “Prehospital 12-lead ECG examination.” The corrected figure appears below.

The flowchart of the AI-based pre-hospital STEMI detection system. Traditionally, after the 12-lead ECG had been recorded in the ambulance vehicle, the ECG data were posted on a secured network for reading by available online physicians as had been usual practice. The time interval between ECG transmission and interpretation feedback by physicians was defined as the physician's response time. In our AI-based pre-hospital STEMI detection system, the recorded signal was also simultaneously transmitted to the AI center of the China Medical University Hospital to be classified “STEMI” or “Not STEMI.” Similarly, the time interval between the ECG transmission and the ECG interpretation feedback by the AI was defined as the AI's response time.

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.