{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T23:44:30Z","timestamp":1789688670532,"version":"4.0.1"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background\/Objectives<\/jats:title>\n                    <jats:p>Rapid triage and etiological differentiation are critical for patients with acute chest pain in the emergency department. The 12-lead electrocardiogram (ECG), as a non-invasive, readily available, and cost-effective diagnostic modality, provides immediate information and serves as the first-line tool for clinical evaluation. However, ECG interpretation remains highly dependent on clinician expertise and is subject to inter-observer variability. Artificial intelligence (AI)-based analytical methods can deliver automated, consistent, and real-time assessment, thereby potentially enhancing diagnostic accuracy and facilitating timely clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 1,188 patients with acute chest pain who visited the emergency department in the Second Xiangya Hospital of Central South University, between March 2024 and March 2025. Standard 12-lead ECGs, clinical information, and final diagnoses were collected. After data preprocessing, a convolutional neural network (CNN) incorporating a channel attention mechanism was developed and trained. Model performance was assessed using accuracy, precision, recall, F1-score, area under the curve (AUC), and confusion matrices. Additionally, a blinded comparative evaluation was conducted against expert cardiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The model demonstrated excellent discriminative capability for ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI), with AUC values of 0.986 and 0.916, respectively. For STEMI, all performance metrics indicated superior diagnostic accuracy, and inference time was significantly shorter than manual interpretation (0.24 \u00b1 0.08 s,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001). However, detection performance for unstable angina (UA) and aortic dissection (AD) remained suboptimal, characterized by high sensitivity but relatively low precision.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The deep learning model based on 12-lead ECGs enables rapid and reliable detection of STEMI and NSTEMI, highlighting its potential as a valuable clinical decision-support tool in emergency department. Nevertheless, the recognition of UA and AD remains limited due to non-specific or transient electrophysiological features.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1746364","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T06:41:34Z","timestamp":1773124894000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and evaluation of an artificial intelligence-based electrocardiogram prediction model for emergency chest pain patients"],"prefix":"10.3389","volume":"13","author":[{"given":"Yazhi","family":"Luo","sequence":"first","affiliation":[{"name":"Hubei Province Key Laboratory of Allergy and Immunology, Taikang Medical School (School of Basic Medical Sciences), Wuhan University","place":["Wuhan, China"]},{"name":"The Second Xiangya Hospital of Central South University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Peng","sequence":"additional","affiliation":[{"name":"The Second Xiangya Hospital of Central South University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Peng","sequence":"additional","affiliation":[{"name":"The Second Xiangya Hospital of Central South University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Second Xiangya Hospital of Central South University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiling","family":"Li","sequence":"additional","affiliation":[{"name":"The Second Xiangya Hospital of Central South University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Province Key Laboratory of Allergy and Immunology, Taikang Medical School (School of Basic Medical Sciences), Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"B1","author":"Walker","year":"1990","journal-title":"Clinical Methods: The History, Physical, and Laboratory Examinations"},{"key":"B2","doi-asserted-by":"publisher","first-page":"302","DOI":"10.31083\/j.rcm2508302","article-title":"How to approach patients with acute chest pain","volume":"25","author":"Inoue","year":"2024","journal-title":"Rev Cardiovasc Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"20220307","DOI":"10.1259\/bjr.20220307","article-title":"Role of cardiac imaging in acute chest pain","volume":"96","author":"Koh","year":"2023","journal-title":"Br J Radiol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.echo.2023.01.015","article-title":"Video-based deep learning for automated assessment of left ventricular ejection fraction in pediatric patients","volume":"36","author":"Reddy","year":"2023","journal-title":"J Am Soc Echocardiogr"},{"key":"B5","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"B6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1093\/ehjdh\/ztad074","article-title":"International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction","volume":"5","author":"Herman","year":"2024","journal-title":"Eur Heart J Digit Health"},{"key":"B7","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1093\/ehjdh\/ztab029","article-title":"Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram","volume":"2","author":"Chang","year":"2021","journal-title":"Eur Heart J Digit Health"},{"key":"B8","doi-asserted-by":"publisher","first-page":"153862","DOI":"10.1016\/j.jelectrocard.2024.153862","article-title":"12 lead surface ECGs as a surrogate of atrial electrical remodeling - 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