The emergence of digital twin technology is transforming healthcare by shifting the paradigm from reactive to proactive care. These sophisticated, personalized virtual models of human physiology are especially impactful in simulating vascular systems, enabling the noninvasive diagnosis and treatment of conditions like stenosis. However, practical deployment in clinical settings faces significant challenges, such as managing vast datasets, computational complexity, and the need for real-time processing. This talk will focus on how high-performance computing addresses these obstacles through the development of the Longitudinal Hemodynamic Mapping Framework (LHMF). Unlike current models limited to single-heartbeat simulations, LHMF enables continuous, long-term hemodynamic analysis, simulating over 4.5 million heartbeats with negligible error. I will present its deployment on both traditional and cloud-based platforms, demonstrating its scalability and clinical relevance.
Furthermore, the integration of continuous physiological data from wearable technologies with digital twin models offers new possibilities for monitoring disease progression over time. This synergy, combined with advances in virtual reality, enhances clinician interaction with complex models, facilitating early disease detection and personalized treatment planning. By leveraging advanced computational methods and digital health innovations, digital twins have the potential to revolutionize healthcare, driving a proactive approach that improves patient outcomes.
Amanda Randles is the Alfred Winborne Mordecai and Victoria Stover Mordecai Associate Professor of Biomedical Sciences and Biomedical Engineering at Duke University. She has courtesy appointments in the Mechanical Engineering and Material Science, Computer Science, and Mathematics departments and is a member of the Duke Cancer Institute. Focusing on the intersection of high performance computing, machine learning, and personalized modeling, her group is developing new methods to aid in diagnosing and treating diseases ranging from cardiovascular disease to cancer. She has received the ACM Prize in Computing, the NIH Pioneer Award, the NSF CAREER Award, and the ACM Grace Hopper Award. She was named to the World Economic Forum Young Scientist List and the MIT Technology Review World’s Top 35 Innovators under the Age of 35 list and is a Fellow of the National Academy of Inventors. Amanda received her Ph.D. in Applied Physics from Harvard University. Before that, she received her Master’s degree in Computer Science from Harvard University and her Bachelor’s degree in Computer Science and Physics from Duke University. Prior to graduate school, she worked as a software engineer at IBM on the Blue Gene supercomputing team. She has contributed to over 100 peer-reviewed papers, 121 granted US patents, and has about 75 pending patent applications.
