Background and Objectives This paper presents the results of a Machine-Learning based Model Order Reduction (MOR) method applied to a complex 3D Finite Element (FE) biomechanical model of the human tongue, in order to create a Digital Twin Model (DTM) that enables real-time simulations. The DTM is designed for future inclusion in a computer assisted protocol for tongue surgery planning.

Methods The proposed method uses an “a posteriori” MOR that allows, from a limited number of simulations with the FE model, to predict in real time mechanical responses of the human tongue to muscle activations.

Results. The MOR method is evaluated for simulations associated with separate single tongue muscle activations. It is shown to be able to account with a sub-millimetric spatial accuracy for the non-linear dynamical behavior of the tongue model observed in these simulations.

Conclusion Further evaluations of the MOR method will include tongue movements induced by multiple muscle activations. At this stage our MOR method offers promising perspectives for the use of the tongue model in a clinical context to predict the impact of tongue surgery on tongue mobility. As a long term application, this DTM of the tongue could be used to predict the functional consequences of the surgery in terms of speech production and swallowing.

背景和目的本文介绍了一种基于机器学习的模型降阶（MOR）方法的结果，该方法应用于人舌的复杂3D有限元（FE）生物力学模型，目的是创建一个数字孪生模型（DTM）启用实时仿真。DTM旨在将来纳入舌头手术计划的计算机辅助协议中。

方法所提出的方法使用“后验” MOR，它可以使用有限元模型进行有限次数的模拟，从而实时预测人舌对肌肉激活的机械反应。

结果。对MOR方法进行了评估，以进行与单独的单舌肌肉激活相关的模拟。结果表明，对于这些模拟中观察到的舌头模型的非线性动力学行为，它能够以亚毫米级的空间精度进行解释。

结论对MOR方法的进一步评估将包括多次肌肉激活引起的舌头运动。在此阶段，我们的MOR方法为在临床情况下使用舌头模型预测舌头手术对舌头活动性的影响提供了有希望的观点。作为长期的应用，这种DTM舌头可用于预测手术在语音产生和吞咽方面的功能后果。