Time-resolved prediction of dental implant biomechanics through integration of finite element analysis,...

Dental implant longevity depends on the complex interaction between mechanical stability and biological osseointegration. While finite element analysis (FEA) provides valuable mechanical insight, it remains static and computationally expensive.

This study presents a hybrid time-resolved computational framework combining finite element data, osseointegration dynamics, and deep learning to predict the biomechanical behavior of titanium dental implants throughout the healing process.

A parametric 3D FEA model simulated 800 implant-bone configurations varying in geometry, loading, and bone quality. A mechanobiological model of osseointegration described the monthly evolution of bone density, bone-implant contact (BIC), and interfacial stiffness over 12 months. These temporal variables were integrated into a hybrid Multilayer Perceptron - Long Short-Term Memory (MLP-LSTM) neural network - designed to capture both spatial and time-dependent features-trained to predict von Mises stress (σ_VM), maximum displacement (δ_max), and fatigue safety factor (FSF, an indicator of long-term structural failure risk).

The model achieved R² > 0.98 for all outputs and mean absolute errors <0.015. Temporal simulation revealed that interfacial stiffness increased by 270 %, while FSF declined nonlinearly with load above 200 N. Predictions were generated in <0.01 s per case, offering >4000 × speed-up compared to conventional FEA.

The framework captures both mechanical and biological evolution of the implant-bone interface, providing physiologically realistic, computationally efficient predictions. This approach represents a step toward personalized, AI-assisted implant design and load management. Clinically, this tool allows for rapid pre-surgical screening of implant designs against patient-specific risk factors. Limitations include the reliance on synthetic data derived from simplified bone geometries, static loading assumptions, and unvalidated mechanobiological parameters, necessitating future in vivo validation. These findings represent a computational proof-of-concept and require validation against patient-specific geometries and biological data before clinical adoption.