Salvador Matteo, Strocchi Marina, Regazzoni Francesco, Augustin Christoph M, Dede' Luca, Niederer Steven A, Quarteroni Alfio
Institute for Computational and Mathematical Engineering, Stanford University, California, CA, USA.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
NPJ Digit Med. 2024 Apr 11;7(1):90. doi: 10.1038/s41746-024-01084-x.
Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor.
心脏数字孪生体提供了一个基于物理和生理学的框架来实现个性化医疗。然而，由于其高昂的计算成本，高保真多尺度心脏模型仍然是推广应用的一个障碍。基于人工智能的方法可以使创建快速且准确的全心脏数字孪生体成为可能。我们使用潜在神经常微分方程（LNODEs）来学习一名心力衰竭患者的压力-容积动态变化。我们的替代模型是从400次模拟中训练得到的，同时考虑了描述细胞到器官心脏机电学和心血管血液动力学的43个参数。LNODEs通过一个人工神经网络在潜在空间中提供3D-0D模型的紧凑表示，该网络仅保留3个隐藏层，每层有13个神经元，并允许在单个处理器上对心脏功能进行数值模拟。我们使用LNODEs在3小时的计算中（仍然在单个处理器上）进行全局敏感性分析和带有不确定性量化的参数估计。
