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.

心脏数字双胞胎提供了一个物理和生理学知识框架来提供个性化医疗。然而，高保真多尺度心脏模型由于其大量的计算成本仍然是采用的障碍。基于人工智能的方法可以使快速、准确的全心脏数字双胞胎的创建成为可能。我们使用潜在神经常微分方程 (LNODE) 来了解心力衰竭患者的压力-容积动态。我们的代理模型经过 400 次模拟训练，同时考虑了描述细胞到器官心脏机电和心血管血流动力学的 43 个参数。 LNODE 通过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 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 LNODE 在 3 小时的计算中执行全局灵敏度分析和参数估计，并进行不确定性量化，而且仍然在单个处理器上进行。