{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:08:15Z","timestamp":1788480495604,"version":"build-2803163510"},"reference-count":59,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01HL142893"],"award-info":[{"award-number":["R01HL142893"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["physoc.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Physiology"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec><jats:title>Abstract<\/jats:title><jats:p>Personalized, image\u2010based computational heart modelling is a powerful technology that can be used to improve patient\u2010specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state\u2010of\u2010the\u2010art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning\u2010based workflow for reconstructing personalized computational electrophysiological heart models to guide patient\u2010specific treatment of VT. Contrast\u2010enhanced computed tomography (CE\u2010CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE\u2010CT was developed, trained and evaluated. From both CNN\u2010based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN\u2010based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN\u2010based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co\u2010localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning\u2010based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly\u2010derived heart models. Hence, a user\u2010independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy.\n<jats:boxed-text content-type=\"graphic\" position=\"anchor\"><jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/tjp15554-gra-0001-m.png\"><jats:alt-text>image<\/jats:alt-text><\/jats:graphic><\/jats:boxed-text><\/jats:p><\/jats:sec><jats:sec><jats:title>Key points<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Personalized electrophysiological heart modelling can aid in patient\u2010specific ventricular tachycardia (VT) risk stratification and VT ablation targeting.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Current state\u2010of\u2010the\u2010art, image\u2010based heart models for VT prediction require expert\u2010dependent, manual interactions that may not be accessible across clinical settings.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>In this study, we develop an automated, deep learning\u2010based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert\u2010generated heart models.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>The number and location of VTs was similar between heart models generated from the deep learning\u2010based workflow and expert\u2010generated heart models.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1113\/jp284125","type":"journal-article","created":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T09:23:30Z","timestamp":1681550610000},"page":"4625-4644","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of a deep learning\u2010enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias"],"prefix":"10.1113","volume":"602","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9918-0133","authenticated-orcid":false,"given":"Eric","family":"Sung","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore MD USA"},{"name":"Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University  Baltimore MD 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