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

Abstract

Mechanical complications in dental implantology often arise from a mismatch between standardized geometries and patient-specific anatomical constraints. While high-fidelity finite element analysis (FEA) is the gold standard for identifying these risks, its high computational cost creates a “bottleneck” that prevents its use in routine surgical planning. To develop a numerical inverse design framework that overcomes this limitation by accelerating the generation of optimal implant geometries through a neural-surrogate-assisted optimization engine. A high-fidelity dataset of 3000 high-fidelity 3D FEA simulations was used to train a multilayer perceptron (MLP) regressor ($R^2=0.9673$). This model served as a real-time inference engine replacing expensive iterative simulations within surrogate-assisted evolutionary optimization framework. The framework's accuracy and clinical validity were tested on a synthetic cohort of 50 virtual patients representing diverse bone qualities and loading conditions. The accelerated inverse design system achieved a statistically significant and substantial reduction in peak von Mises stress at the bone–implant interface compared with standard clinical protocols (, Cohen's d = 3.22). The optimization successfully identified patient-specific compensatory strategies, such as prescribing wider diameters for low-density bone to maximize load distribution. By integrating deep learning surrogates to accelerate complex numerical optimization, this framework enables real-time, patient-specific implant prescription. This methodology effectively bypasses the computational burden of traditional FEA, offering a scalable numerical solution for personalized surgical planning.