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ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1462847
This article is part of the Research Topic Advances in Artificial Intelligence-Enhanced Electrocardiography: A Pathway towards Improved Diagnosis and Patient Care. View all 4 articles

ECG Data Analysis to Determine ST-segment Elevation Myocardial Infarction and Infarction Territory Type: An Integrative Approach of Artificial Intelligence and Clinical Guidelines

Provisionally accepted
Jong Kwang Kim Jong Kwang Kim 1Byungeun Shon Byungeun Shon 2Sangwook Kim Sangwook Kim 3Jungrae Cho Jungrae Cho 2JungJu Seo JungJu Seo 2SeYong Jang SeYong Jang 4*Sungmoon Jeong Sungmoon Jeong 1*
  • 1 Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
  • 2 Research Center for AI in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
  • 3 Bio-medical Research Institute, Kyungpook National University Hospital, Daegu, Republic of Korea
  • 4 Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea

The final, formatted version of the article will be published soon.

    Abstract Acute coronary syndrome (ACS) is a leading cause of death from cardiovascular diseases worldwide, and ST-segment elevation myocardial infarction (STEMI) represents a severe form of ACS with high prevalence and mortality rates. This study proposes a novel method using deep learning-based artificial intelligence (AI) algorithms to diagnose the STEMI accurately from 12-lead electrocardiogram (ECG) data and to classify the type of infarction territory in detail. Based on an ECG database of 888 MI patients, the study enhanced the generalization capability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately detected STE, a crucial indicator in the clinical ECG diagnosis of STEMI, in each of the 12 leads. Utilizing this detector in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI) showed significant results, with an average AUROC of 0.939 and AUPRC of 0.977. Moreover, this detector demonstrated accurate differentiation capabilities for each infarction territory in the ECG, such as anterior myocardial infarction (AMI), inferior MI (IMI), lateral MI (LMI), and suspected left main disease. These results suggest that using AI technology in ECG diagnoses can play an important role in the rapid treatment and prognosis improvement of STEMI patients.

    Keywords: ST-segment elevation detection, deep learning-based artificial intelligence, STsegment elevation myocardial infarction, 12-lead electrocardiogram, infarction territory

    Received: 16 Jul 2024; Accepted: 19 Sep 2024.

    Copyright: © 2024 Kim, Shon, Kim, Cho, Seo, Jang and Jeong. 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) or licensor 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:
    SeYong Jang, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
    Sungmoon Jeong, Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea

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