Deep-Learning-Based Prediction of Occlusal Stresses on Teeth
Résumé
We propose a deep-learning approach involving the training of two neural networks to enable real-time simulations while maintaining sufficient accuracy for educational purposes. The training data was generated from finite element simulations by varying parameters such as the location and magnitude of the force applied to the tooth.
• We evaluated the performance of two neural networks. The first network was trained to predict the deformations of tooth elements after force application, from which the resulting stresses were then calculated. The second network directly predicted the stresses. Ultimately, the second network proved to be more accurate in stress prediction.
• The approach focuses on visualizing the stresses experienced by teeth during mastication. A deep-learning method trained with biomechanical simulation data permits a real-time visualization. This proof of concept demonstrates the potential for developing educational tools to enhance understanding of occlusion through simulation results.
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