Scientific Abstract Clonal Hematopoiesis of Indeterminate Potential (CHIP) is a condition of aging that has been associated with increased risk of heart disease and heart failure. CHIP is evaluated by DNA sequencing of peripheral blood. However, not all patients with CHIP will develop heart failure or myocardial infarction. Our preliminary data demonstrate that CHIP patients (cancer and non-cancer patients) have a higher burden of nonischemic scarring in their hearts on cardiac MRI compared to patients without CHIP. In a pilot cohort using a machine learning technique of convolutional neural networks (CNN), we are able to predict who have CHIP with 86% accuracy and predicted future cardiomyopathy with an accuracy of 73%, thus opening the door to possible image guided risk stratification for identifying patients with CHIP as well as predict future cardiac outcomes. We also find through our novel deep learning algorithm that we can identify extended somatic variants that are CHIP-like that can impact heart failure outcomes and through integration of patient data, genomic and imaging signatures, we are able to develop partition risk scores that can weigh the variables such as CHIP and their contribution to heart failure and the pathways involved. We hypothesize that using cardiac MRI, novel machine learning techniques on large imaging datasets can identify CHIP vs non-CHIP patients as well as predict who will develop adverse cardiovascular outcomes and that CNNs can be used on both imaging and genomic signatures in large cohorts like the TOPMED, UKB and All of Us to predict cardiovascular outcomes while pathway analyses will reveal mechanisms and novel targets for CHIP-mediated cardiomyopathy Specific Aim #1 will use a novel CNN with multi-view cross-attention to identify MRI features that can accurately predict CHIP as well as future adverse outcomes such as heart failure and myocardial infarction that will be validated in large datasets like TOPMed, UKB and All of Us with a collective ~13000 cardiac MRIs. Specific Aim #2 will use artificial intelligence approaches and a novel analysis pipeline that combines patient data, cardiac MRI signatures and genomic signatures of CHIP and extended somatic variants that are predictive of cardiovascular outcomes to help reveal mechanisms behind CHIP’s contribution to cardiomyopathy.