FDT-BioTech: Scalable Digital Twins to Power Virtual Trials and Test Treatments

Duke University, Durham NC

Investigators

Abstract

Cardiovascular disease is the leading cause of death globally, yet many diagnostic and treatment decisions rely on costly or invasive procedures. This project develops advanced computer models that act like “virtual replicas” of a patient’s blood vessels and heart activity. Using everyday heart-rate data from wearable sensors, the investigators simulate how blood flows and interacts with vessel walls over weeks or months, all on powerful computers. The investigators also build tools to capture how each person’s vessel shape and branch patterns vary and then create large groups of realistic virtual patients to test devices and therapies without risk. By replacing or reducing the need for invasive tests and accelerating the evaluation of medical devices, this work promises earlier detection of heart problems, more personalized care, and faster delivery of safer treatments to patients. This project addresses the need for scalable, high-fidelity assessment of how anatomical variability influences hemodynamic biomarkers. The investigators will build a computational framework that systematically surveys changes in vascular geometry and quantifies their impact on key fluid‐dynamics‐derived metrics—such as wall shear stress, oscillatory shear index, and pressure gradients. First, the research team will cluster patient‐specific models using a Jaccard‐index similarity metric on discretized centerline geometries to narrow the anatomical search space. Next, representative geometries from each cluster will undergo large‐scale, physics‐based simulations on leadership‐class computing resources. By combining clustering‐driven geometry selection with high‐resolution modeling, the investigators will generate spatial and temporal maps linking morphological variation to biomarker distributions across virtual cohorts. This framework enables in silico trials of vascular interventions and supports the discovery of novel computational phenomarkers for personalized cardiovascular care. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.