Machine-Learning based model order reduction of a biomechanical model of the human tongue. (January 2021) - Calka, Maxime; Perrier, Pascal; Ohayon, Jacques; Grivot-Boichon, Christelle; Rochette, Michel; Payan, Yohan
Highlights: Application of Model Order Reduction method to a hyperelastic Finite Element model of the human tongue. Evaluation of the Reduced Order Tongue model on full trajectories of the tongue over time. Approach well suited to modeling biological speech and swallowing movements. Abstract: 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.