Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning | 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 Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning Rana Raza Mehdi, Nikhil Kadivar, Tanmay Mukherjee, Emilio A. Mendiola, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4468678/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance imaging (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited. Health sciences/Health care/Medical imaging Health sciences/Diseases Health sciences/Health care Biological sciences/Biological techniques Biological sciences/Biological techniques/Imaging Biological sciences/Biological techniques/Imaging/Magnetic resonance imaging Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Image processing Physical sciences/Engineering Physical sciences/Engineering/Biomedical engineering Full Text Additional Declarations Competing interest reported. GK discloses financial interests with the companies Anailytica and PredictiveIQ. Other authors declare no conflict of interest. Supplementary Files Supplementary.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4468678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307284759,"identity":"7aa058ac-c84f-40d8-aeda-7db3e1176d57","order_by":0,"name":"Rana Raza Mehdi","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"Raza","lastName":"Mehdi","suffix":""},{"id":307284760,"identity":"312d5ff7-d5c3-460a-b8e0-0c7b12cc622b","order_by":1,"name":"Nikhil Kadivar","email":"","orcid":"","institution":"Brown University","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"","lastName":"Kadivar","suffix":""},{"id":307284761,"identity":"d479aa91-9e6f-478c-a772-022ffd2fc3a8","order_by":2,"name":"Tanmay Mukherjee","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Tanmay","middleName":"","lastName":"Mukherjee","suffix":""},{"id":307284762,"identity":"8c5417fb-4944-4b2c-a7ad-a687c4196c4d","order_by":3,"name":"Emilio A. 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