{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T19:29:58Z","timestamp":1786649398714,"version":"build-2736575974"},"reference-count":21,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVES:<\/jats:title>\n                    <jats:p>Accurate differentiation between ventricular tachycardia (VT) and supraventricular tachycardia (SVT) with aberrant conduction in wide complex tachyarrhythmias (WCT) remains a significant challenge in emergency medicine. This study aimed to evaluate the efficacy of deep learning (DL) models, specifically pretrained residual network (ResNet) architectures, in classifying these arrhythmias using electrocardiography (ECG) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>A retrospective cross-sectional study was conducted, analysing 652 WCT ECGs and 248 normal sinus rhythm ECGs from an emergency medicine clinic. Three ResNet models ResNet-18, ResNet-34, and ResNet-50 were fine-tuned using transfer learning. Model performance was assessed via 10-fold cross-validation, evaluating accuracy, sensitivity, and precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>All ResNet models demonstrated high and consistent performance, achieving 95% accuracy, precision in distinguishing VT from SVT with aberrant conduction. The models exhibited robust generalization across validation folds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION:<\/jats:title>\n                    <jats:p>DL models, particularly ResNet architectures, show promise in enhancing ECG-based diagnosis of WCT. Their integration into emergency care could improve diagnostic accuracy, especially in settings with limited access to specialized cardiac expertise.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/tjem.tjem_74_25","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T03:01:42Z","timestamp":1759374102000},"page":"288-296","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Potential of deep learning in advancing electrocardiography arrhythmia diagnosis in emergency medicine"],"prefix":"10.4103","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2931-775X","authenticated-orcid":false,"given":"Can Berk","family":"Inan","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Tokat Erbaa State Hospital, Tokat, United 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