Kashtanova Victoriya, Pop Mihaela, Ayed Ibrahim, Gallinari Patrick, Sermesant Maxime
Inria Université Côte d'Azur, Nice, France.
3IA Côte d'Azur, Sophia Antipolis, France.
Interface Focus. 2023 Dec 15;13(6):20230043. doi: 10.1098/rsfs.2023.0043. eCollection 2023 Dec 6.
Modelling complex systems, like the human heart, has made great progress over the last decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias and personalizing treatments. However, building highly accurate predictive heart models requires a delicate balance between mathematical complexity, parameterization from measurements and validation of predictions. Cardiac electrophysiology (EP) models range from complex biophysical models to simplified phenomenological models. Complex models are accurate but computationally intensive and challenging to parameterize, while simplified models are computationally efficient but less realistic. In this paper, we propose a hybrid approach by leveraging deep learning to complete a simplified cardiac model from data. Our novel framework has two components, decomposing the dynamics into a physics based and a data-driven term. This construction allows our framework to learn from data of different complexity, while simultaneously estimating model parameters. First, using data, we demonstrate that this framework can reproduce the complex dynamics of cardiac transmembrane potential even in the presence of noise in the data. Second, using optical data of action potentials (APs), we demonstrate that our framework can identify key physical parameters for anatomical zones with different electrical properties, as well as to reproduce the AP wave characteristics obtained from various pacing locations. Our physics-based data-driven approach may improve cardiac EP modelling by providing a robust biophysical tool for predictions.
在过去几十年中，对诸如人类心脏这样的复杂系统进行建模取得了巨大进展。针对特定患者的模型，即所谓的“数字孪生”，有助于心律失常的诊断和治疗的个性化。然而，构建高度准确的心脏预测模型需要在数学复杂性、基于测量的参数化以及预测验证之间实现微妙的平衡。心脏电生理学（EP）模型范围从复杂的生物物理模型到简化的唯象模型。复杂模型准确但计算量大且参数化具有挑战性，而简化模型计算效率高但不太现实。在本文中，我们提出了一种混合方法，通过利用深度学习从数据中完成一个简化的心脏模型。我们的新框架有两个组成部分，将动力学分解为基于物理的项和数据驱动的项。这种构建方式使我们的框架能够从不同复杂性的数据中学习，同时估计模型参数。首先，使用数据，我们证明即使在数据存在噪声的情况下，该框架也能重现心脏跨膜电位的复杂动力学。其次，使用动作电位（AP）的光学数据，我们证明我们的框架可以识别具有不同电学特性的解剖区域的关键物理参数，以及重现从各种起搏位置获得的AP波特征。我们基于物理的数据驱动方法可能通过提供一个强大的生物物理预测工具来改进心脏EP建模。
