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The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post\u2010MI care. Late gadolinium enhancement\u2010cardiac magnetic resonance (LGE\u2010CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE\u2010CMR is the invasive intravenous introduction of gadolinium\u2010based contrast agents that present potential high\u2010risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, a completely non\u2010invasive methodology is developed to identify 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 approach, the remarkable performance of a multi\u2010fidelity ML model is demonstrated, which combines rodent\u2010based in\u2010silico\u2010generated training data (low\u2010fidelity) with very limited patient\u2010specific human data (high\u2010fidelity) in predicting LGE ground truth. The results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation\u2010based 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\u00a0limited.<\/jats:p>","DOI":"10.1002\/advs.202406933","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T06:01:45Z","timestamp":1750831305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Non\u2010Invasive Diagnosis of Chronic Myocardial Infarction via Composite In\u2010Silico\u2010Human Data Learning"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9008-9058","authenticated-orcid":false,"given":"Rana Raza","family":"Mehdi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Texas A&amp;M University College Station TX 77843 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikhil","family":"Kadivar","sequence":"additional","affiliation":[{"name":"School of Engineering Brown University Providence RI 02912 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanmay","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Texas A&amp;M University College Station TX 77843 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilio A.","family":"Mendiola","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Texas A&amp;M University College Station TX 77843 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akila","family":"Bersali","sequence":"additional","affiliation":[{"name":"Houston Methodist DeBakey Heart &amp; Vascular Center Houston TX 77030 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dipan J.","family":"Shah","sequence":"additional","affiliation":[{"name":"Houston Methodist DeBakey Heart &amp; Vascular Center Houston TX 77030 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Karniadakis","sequence":"additional","affiliation":[{"name":"School of Engineering Brown University Providence RI 02912 USA"},{"name":"Division of Applied Mathematics Brown University Providence RI 02912 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9787-1117","authenticated-orcid":false,"given":"Reza","family":"Avazmohammadi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Texas A&amp;M University College Station TX 77843 USA"},{"name":"School of Engineering Medicine Texas A&amp;M University Houston TX 77030 USA"},{"name":"J. 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