Precise ECG Diagnosis and Validation of Educational Utility for Acute Myocardial Infarction Using Deep Learning and Explainable Artificial Intelligence

Precise ECG Diagnosis and Validation of Educational Utility for Acute Myocardial Infarction Using Deep Learning and Explainable Artificial Intelligence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Precise ECG Diagnosis and Validation of Educational Utility for Acute Myocardial Infarction Using Deep Learning and Explainable Artificial Intelligence Jongkwang Kim, Byungeun Shon, Yongjin Kim, Nayeon Kim, Yejin Jang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8217685/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocardial infarction (NSTEMI) and overcoming the "black box" nature of deep learning models remain persistent challenges. Here, we present a comprehensive deep learning framework capable of classifying STEMI, NSTEMI, and non-acute coronary syndrome (non-ACS) from 12-lead ECG images, while also localizing infarction sites. Utilizing ,2070 validated ECGs, our pipeline integrates ResNet for acute myocardial infarction detection, Faster R-CNN for ST-segment elevation localization, and an ensemble approach for final classification. The model achieved a 98.3% AUROC for detection and an overall three-class accuracy of 93.6%, with high F1 scores for identifying infarction territories. To address interpretability, we developed an explainable AI (XAI) web viewer that visualizes detected regions. Furthermore, we evaluated the model’s utility as an educational tool in a prospective pilot study with medical students. AI assistance significantly improved the students' overall diagnostic accuracy from 43% to 82% (p<0.05), with notable gains in identifying NSTEMI and complex STEMI subtypes. These findings demonstrate that our interpretable AI model not only supports clinical decision-making with high diagnostic precision but also serves as an effective educational aid for enhancing novice clinicians' proficiency. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Health care Artificial intelligence Electrocardiograms ST-segment elevation myocardial infarction non-ST-elevation myocardial infarction Explainable AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 04 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 29 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8217685","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626901832,"identity":"7b49a71c-c71e-4b41-ad80-6bd88d41fe0a","order_by":0,"name":"Jongkwang Kim","email":"","orcid":"","institution":"Kyungpook National University","correspondingAuthor":false,"prefix":"","firstName":"Jongkwang","middleName":"","lastName":"Kim","suffix":""},{"id":626901834,"identity":"680b23a3-178a-430c-b65c-92e70f853152","order_by":1,"name":"Byungeun Shon","email":"","orcid":"","institution":"Kyungpook National University 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