Numerical Inverse Design of Patient‐Specific Dental Implants: Accelerating <scp>FEA</scp> ‐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 (). 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.