Pré-Publication, Document De Travail Année : 2025

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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Dates et versions

hal-05416912 , version 1 (15-12-2025)

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  • HAL Id : hal-05416912 , version 1

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Doriane Ajmi, Maxime Ducret, Benjamin Gilles, Raphaël Richert, Florence Zara. Deep-Learning-Based Prediction of Occlusal Stresses on Teeth. 2025. ⟨hal-05416912⟩
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