{"doi":"10.1002/advs.202406933","title":"Non‐Invasive Diagnosis of Chronic Myocardial Infarction via Composite In‐Silico‐Human Data Learning","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 (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, a completely non-invasive 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-fidelity ML model is demonstrated, which combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. The 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.","journal":"Advanced Science","year":2025,"id":513418,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.916,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1333207,"name":"Nikhil Kadivar","orcid":"0009-0003-2904-8681","position":1,"is_corresponding":false},{"id":1163289,"name":"Tanmay Mukherjee","orcid":"0000-0002-0191-3878","position":2,"is_corresponding":false},{"id":873815,"name":"Emilio A. Mendiola","orcid":"0000-0002-7774-0811","position":3,"is_corresponding":false},{"id":1374463,"name":"Akila Bersali","orcid":"0000-0003-2217-1567","position":4,"is_corresponding":false},{"id":345237,"name":"Dipan J. Shah","orcid":"0000-0002-6179-2393","position":5,"is_corresponding":false},{"id":54952,"name":"George Em Karniadakis","orcid":"0000-0002-9713-7120","position":6,"is_corresponding":false},{"id":340096,"name":"Reza Avazmohammadi","orcid":"0000-0001-9787-1117","position":7,"is_corresponding":false},{"id":1163290,"name":"Rana Raza Mehdi","orcid":"0000-0002-9008-9058","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-19T02:48:17.908238Z","pmid":"40536227","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}