Integrating Finite Element Data with Neural Networks for Fatigue Prediction in Titanium Dental Implants: A Proof-of-Concept Study

Abstract

Background: Titanium dental implants are widely used, but their long-term mechanical reliability under fatigue loading remains a key concern. Traditional finite element analysis is accurate but computationally intensive. This study explores the integration of finite element analysis data with neural networks to predict fatigue-related responses efficiently. Methods: A dataset of 200 finite element analysis simulations was generated, varying load intensity, load angle, and implant size. Each simulation provided three outputs: maximum von Mises stress, maximum displacement, and fatigue safety factor. A feedforward neural network with two hidden layers (64 neurons each, ReLU activation) was trained using 160 simulations, with 40 reserved for testing. Results: The neural network achieved high accuracy across all outputs, with R2 values of 0.97 for stress, 0.95 for deformation, and 0.92 for the fatigue safety factor. Mean errors across the test set were below 5%, indicat- ing strong predictive performance under diverse conditions. Conclusions: The findings demonstrate that neural networks can reliably replicate finite element analysis outcomes with significantly reduced computational time. This approach offers a promising tool for accelerating implant assessment and supports the growing role of AI in biomechanical design and analysis.