Cardiac digital twins provide a physics and physiology informed framework to deliver predictive and personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs and the high number of model evaluations needed for patient-specific personalization. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. In this work, we use Latent Neural Ordinary Differential Equations (LNODEs) to learn the temporal pressure-volume dynamics of a heart failure patient. Our surrogate model based on LNODEs is trained from 400 3D-0D whole-heart closed-loop electromechanical simulations while accounting for 43 model parameters, describing single cell through to whole organ and cardiovascular hemodynamics. The trained LNODEs provides a compact and efficient representation of the 3D-0D model in a latent space by means of a feedforward fully-connected Artificial Neural Network that retains 3 hidden layers with 13 neurons per layer and allows for 300x real-time numerical simulations of the cardiac function on a single processor of a standard laptop. This surrogate model is employed to perform global sensitivity analysis and robust parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. We match pressure and volume time traces unseen by the LNODEs during the training phase and we calibrate 4 to 11 model parameters while also providing their posterior distribution. This paper introduces the most advanced surrogate model of cardiac function available in the literature and opens new important venues for parameter calibration in cardiac digital twins.

翻译：心脏数字孪生为预测性和个性化医疗提供了基于物理和生理学的框架。然而，高保真度多尺度心脏模型因其高昂的计算成本以及患者个体化校准所需的多次模型评估，而仍难以广泛应用。基于人工智能的方法能够实现快速且准确的全心数字孪生构建。在本工作中，我们采用潜在神经常微分方程来学习心力衰竭患者的时间-压力-容积动态。基于潜在神经常微分方程的替代模型从400次三维-零维全封闭环机电模拟中训练而成，并考虑了43个模型参数，涵盖从单细胞到整个器官及心血管血液动力学的描述。训练后的潜在神经常微分方程通过一个前馈全连接人工神经网络，在潜在空间中对三维-零维模型提供了紧凑且高效的表示——该网络保留3个隐藏层，每层13个神经元，可在标准笔记本电脑的单个处理器上实现300倍真实时间的心脏功能数值模拟。该替代模型被用于在单个处理器上仅用3小时计算完成全局灵敏度分析及带不确定性量化的鲁棒参数估计。我们成功匹配了训练阶段潜在神经常微分方程未见过的压力和容积时间轨迹，并校准了4至11个模型参数，同时提供了它们的后验分布。本文提出了现有文献中最先进的心脏功能替代模型，并为心脏数字孪生中的参数校准开辟了重要新途径。