Numerical Inverse Design of Patient-Specific Dental Implants: Accelerating FEA-Based Optimization via Evolutionary...

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Dental Implantology

Background:

• Mechanical complications in dental implants stem from mismatches between standard designs and patient anatomy.
• High-fidelity finite element analysis (FEA) is accurate but computationally expensive, hindering its use in routine surgical planning.

Purpose of the Study:

• To develop an accelerated numerical inverse design framework for optimizing dental implant geometries.
• To overcome the computational limitations of traditional FEA for patient-specific surgical planning.

Main Methods:

• Trained a multilayer perceptron (MLP) regressor using 3000 FEA simulations to create a neural surrogate.
• Integrated the MLP into a surrogate-assisted evolutionary optimization framework to replace iterative simulations.
• Validated the framework on 50 virtual patients with varying bone qualities and loading conditions.

Main Results:

• The MLP regressor achieved high accuracy ($R^2=0.9673$).
• The accelerated system significantly reduced peak von Mises stress at the bone-implant interface compared to standard protocols (, Cohen's d=3.22).
• Identified patient-specific strategies, like wider implants for low-density bone, for optimal load distribution.

Conclusions:

• Deep learning surrogates can accelerate complex numerical optimization for dental implant design.
• This framework enables real-time, patient-specific implant prescription, bypassing FEA computational burdens.
• Offers a scalable numerical solution for personalized dental surgical planning.