SCH: SciML-Accelerated Cardiovascular Digital Twins for Personalizing Long-Term Heart Valve Interventions

Valvular heart diseases, such as mitral valve regurgitation, significantly impact patient health and require personalized therapeutic strategies for long-term success. Current interventions involve valve repair or replacement through surgical or minimally invasive procedures aimed at restoring normal valve function. However, optimizing these treatments for individual patients remains challenging due to the complexity of cardiac and valvular mechanics. Although computational modeling, particularly finite element analysis, has enhanced ou r understanding of cardiovascular dynamics, its computati onal intensity limits practical use in real-time clinical decision-making and long-term patient monitoring. This research aims to advance personalized cardiovascular care by developing a rapid, predictive, and adaptive digital twin technology capable of forecasting heart valve disease progression, optimizing interventions, and improving therapeutic outcomes. Our central hypothesis is that integrating advanced scientific machine learning (SciML) with computational modeling will substantially accelerate simulation speeds, enabling fast and predictive modeling of cardiac biomechanics, hemodynamics, and fluid-structure interactions (FSI), which are essential for long-term optimization of heart valve interventions. To achieve this, we propose developing a neural network finite element (NNFE) framework to significantly accelerate complex multiphysics heart valve simulations for adaptive, personalized treatment planning. Specifically, Aim 1 focuses on developing an efficient neural network-driven FE model for structural cardiac mechanics and valve dynamics, while Aim 2 extends this NNFE technology to simulate multiphysics cardiovascular interactions involving patient-specific hemodynamics. Aim 3 integrates these NNFE models into a comprehensive digital twin platform ta rgeting mitral valve regurgitation, assessing the effectiveness of Transcatheter Edge-to-Edg e Repair (T E E R), and examining the impacts of pre- and post-operative valve geometry, mechanics, and blood flow on long-term clinical performance. Ultimately, the proposed heart valve digital twin will enable clinicians to predict disease progression, optimize valve interventions, and improve patient outcomes. This project represents a transformative advancement in personalized medicine, aiming to improve healthcare delivery, reduce costs, and enhance the quality of life for cardiovascular patients. RELEVANCE (See instructions): Heart valve disease is a serious condition that can greatly affect a person's health and daily life if not managed properly. This research uses advanced computer simulations and artificial intelligence to create a digital heart model that quickly and accurately predicts how individual patients will respond to different treatments. By enabling personalized therapies and providing faster insights into treatment outcomes, this approach could improve patient care, lower healthcare costs, and enhance overall quality of life.