Collaborative Research: FDT-BioTech: Promoting Healthy Cardiovascular Aging through Personalized Multiscale Digital Twins

Cardiovascular aging, which involves various structural and functional changes in the vascular, valvular, and ventricular systems, is a significant risk factor for heart diseases and associated morbidities. These conditions typically progress silently and become noticeable only in advanced stages, making early detection and intervention crucial. Traditional diagnostic methods, which rely on symptoms to guide further testing, are increasingly challenged by a growing patient population and a short-staffed clinical workforce. This project aims to transform aging care by developing personalized digital twin technology that will integrate data from wearable devices, echocardiographic measurements, and advanced cardiovascular modeling. This initiative will enhance the monitoring and understanding of cardiovascular aging through noninvasive methods, allowing for better therapeutic interventions. The digital twin technology will have broad applications in healthy aging care and disease monitoring. Additionally, the project will provide educational opportunities in mathematics, scientific computing, and biomedical science, promoting diversity and inclusion in STEM fields. Outreach efforts will emphasize the importance of healthy lifestyles and scientific literacy to the broader community The central theme of this project is to utilize computational and animal models to develop a physics-based personalized digital twin for monitoring and understanding cardiovascular aging. By integrating subject-specific simulations, artificial intelligence, and multiscale noninvasive data, the digital twin will enhance insights into cardiovascular health. The project will focus on developing a data-driven, physics-informed digital twin for real-time monitoring and prediction of aging-related cardiovascular diseases, using mechanics-based markers. Leveraging advanced modeling techniques, scientific machine learning, and noninvasive measurements, this project aims to fill significant knowledge gaps and create a transformative tool for personalized healthcare. The development of novel algorithms for handling multiscale, multimodal data is anticipated to enhance the understanding of mechanical changes associated with cardiovascular aging. Expected outcomes include a physics-based digital tool with predictive capabilities, validated against animal models, offering the potential for early detection and intervention in aging-related cardiovascular diseases. 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.