Friday, May 9, 2025
5/9/2025
Artificial Intelligence Imaging Biomarkers of Longitudinal Cardiovascular Stress - ABSTRACT Across every type of prediction model, applied to virtually any population, the single most substantial contributor to incident cardiovascular disease (CVD) is older age. However, our understanding of why cardiovascular risk dramatically rises with advancing age remains limited. Evidence to date suggests that CVD culminates from age- related changes in cardiac structure and function that begin very early in adulthood and continue to develop on a background of lifelong exposure to risk factors – contributing to an ‘age gap’ between biological age and chronological age that grows over time. It logically follows that a person’s increasing burden of cardiovascular risk should manifest as progressive cardiac abnormalities that can be tracked, analyzed, and identified over the lifespan. Until recently, methods for assessing the cardiovascular ‘age gap’ have been limited. Recent advances in artificial intelligence (AI), when applied to biomedical images, indicate that deep learning algorithms can both offer precise measurements beyond human fidelity and also identify subtle traits in imaging that are unrecognized by the human eye. In fact, our prior work applying AI to echocardiography as well as others applying AI to other forms of medical imaging, have shown deep learning can not only reproduce standard measures of cardiac structural and function but is also identify CVD risk features including chronologic age, biological sex, diabetes, hypertension, and smoking. In effect, AI applied to echocardiography now offers the potential to capture an aggregate measure of cardiac aging and, in turn, identify intervenable target for mitigating age-related CVD risk. Thus, we hypothesize that AI methods applied to echocardiography can be used to not only predict biological cardiovascular age but also (i) identify trajectories of accelerated versus delayed cardiovascular aging, (ii) discern key contributors to accelerated cardiovascular aging phenotypes, and (iii) elucidate the excess age- related risk for specific CVD outcomes that may be independent from any co-existing burden of risk factors or subclinical cardiac disease. To test these hypotheses, we will leverage echocardiography images that are acquired as a part of routine clinical care in addition to serial imaging frequently obtained in large cohort studies of aging adults. Because healthcare cohorts are enriched for patients with accelerated aging trajectories, while epidemiologic cohorts are enriched for individuals with delayed aging trajectories, we plan to analyze imaging and outcomes data collected from both types of cohorts. Accordingly, our aims are to train and validate AI models on predicting biological age from cardiac imaging and identify its component contributors to excess cardiovascular risk in both the healthcare setting and in the community setting.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).