Non-Invasive Diagnosis of Chronic Myocardial Infarction via Composite In-Silico-Human Data Learning.
Authors
Affiliations (7)
Affiliations (7)
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
- School of Engineering, Brown University, Providence, RI, 02912, USA.
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, 77030, USA.
- Division of Applied Mathematics, Brown University, Providence, RI, 02912, USA.
- School of Engineering Medicine, Texas A&M University, Houston, TX, 77030, USA.
- J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA.
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, 77030, USA.
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 transformative 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.