• Yifan Zhao, Jing Xiong, Yang Hou, Mengyun Zhu, Yuyan Lu, Yuanxi Xu, Jiadela Teliewubai, Weijing Liu, Xiao Xu, Xin Li, Zheng Liu, Wenhui Peng, Xianxian Zhao, Yi Zhang, and Yawei Xu.
• Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
• Int. J. Cardiol. 2020 Oct 15; 317: 223-230.

AbstractPatient delay is a worldwide unsolved problem in ST-segment elevated myocardial infarction (STEMI). An accurate warning system based on electrocardiogram (ECG) may be a solution for this problem, and artificial intelligence (AI) may offer a path to improve its accuracy and efficiency. In the present study, an AI-based STEMI autodiagnosis algorithm was developed using a dataset of 667 STEMI ECGs and 7571 control ECGs. The algorithm for detecting STEMI proposed in the present study achieved an area under the receiver operating curve (AUC) of 0.9954 (95% CI, 0.9885 to 1) with sensitivity (recall), specificity, accuracy, precision and F1 scores of 96.75%, 99.20%, 99.01%, 90.86% and 0.9372 respectively, in the external evaluation. In a comparative test with cardiologists, the algorithm had an AUC of 0.9740 (95% CI, 0.9419 to 1), and its sensitivity (recall), specificity, accuracy, precision, and F1 score were 90%, 98% and 94%, 97.82% and 0.9375 respectively, while the medical doctors had sensitivity (recall), specificity, accuracy, precision and F1 score of 71.73%, 89.33%, 80.53%, 87.05% and 0.8817 respectively. This study developed an AI-based, cardiologist-level algorithm for identifying STEMI.Copyright © 2020 Elsevier B.V. All rights reserved.

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