It is well known that model-based brain injury criteria present higher potential to predict injury than global head kinematic parameters. Numerical head injury prediction tools are time consuming and require FE-skilled users. To address these difficulties, we suggest applying deep learning techniques to an existing brain FE model. A total of 3,754 head impacts coming from experimental helmet testing were considered as input for the analysis. Each input was expressed in terms of three linear accelerations and three angular velocities versus time, when the target metric was the maximum Von Mises Stress (VMS) computed within the brain via the FE analysis.

The architecture used for the Deep Learning Model (DLM) was the U-Net. The dataset was split into three datasets dedicated for learning and testing. The quality of the DLM was assessed via the Maximum Absolute Error between FEM- and DLM-computed brain maximum VMS. Further, a regression analysis of brain response and injury risk estimated with both methods was conducted. Results showed that the learning process of the network worked adequately and that deep learning techniques were applicable to predict brain response, without any FE analysis, in a realistic way by considering the 6D head kinematic vs time.