{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:01Z","timestamp":1747216141492,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685434"}],"license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,25]]},"abstract":"<jats:p>Cardiac digital twins represent the required functional mechanisms of patient hearts to evaluate therapies and inform clinical decision-making virtually. A scalable generation of cardiac digital twins can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source digital twinning framework for personalising electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG). We extended a Bayesian-based inference framework to infer electrical repolarisation characteristics. Fast simulations are conducted with a decoupled reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics. Parameter uncertainty is represented by inferring a population of ventricular models rather than a single one, which means that parameter uncertainty can be propagated to virtual therapy evaluations. The framework is demonstrated in a healthy female subject, where our inferred reaction-Eikonal models reproduced the patient\u2019s ECG with a Pearson\u2019s correlation coefficient of 0.93. The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing. The tools developed for this study are open-source, ensuring accessibility, inclusivity, and reproducibility, this is available on GitHub.<\/jats:p>","DOI":"10.3233\/faia240422","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:48:18Z","timestamp":1727689698000},"source":"Crossref","is-referenced-by-count":0,"title":["Open Source Cardiac Digital Twinning of Human Ventricular Repolarisation from 12-Lead ECG and MRI"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6491-2565","authenticated-orcid":false,"given":"Julia","family":"Camps","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5325-909X","authenticated-orcid":false,"given":"Zhinuo Jenny","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4187-4970","authenticated-orcid":false,"given":"Ruben","family":"Doste","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8777-0125","authenticated-orcid":false,"given":"Lucas Arantes","family":"Berg","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"given":"Maxx","family":"Holmes","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"given":"Brodie","family":"Lawson","sequence":"additional","affiliation":[{"name":"Department of Physiology, Anatomy & Genetics, University of Oxford, UK"}]},{"given":"Jakub","family":"Tomek","sequence":"additional","affiliation":[{"name":"Queensland University of Technology, Australia"}]},{"given":"Kevin","family":"Burrage","sequence":"additional","affiliation":[{"name":"Department of Physiology, Anatomy & Genetics, University of Oxford, UK"}]},{"given":"Alfonso","family":"Bueno-Orovio","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"given":"Blanca","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:48:18Z","timestamp":1727689698000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"ISBN":["9781643685434"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240422","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}