Genetic algorithm-based optimization of columella shape with FEM surrogate modeling: convergence analysis and application to ossicular chain reconstruction

Ossicular chain reconstruction is a vital surgical approach for restoring hearing in patients with ossicular chain disruption. Despite advancements in prosthetic design, selecting optimal material and geometric configurations remains a challenge due to the complex, nonlinear dynamics of middle ear biomechanics. This study presents a hybrid optimization framework that integrates genetic algorithms (GA) with machine learning-trained finite element (FE) surrogate models to design middle ear columella prostheses with enhanced acoustic performance. Four tympanoplasty models (III c , III i-M , IV c , IV i-M ), based on extended Wullstein classifications, were analyzed using vibroacoustic FE simulations. Surrogate models based on Random Forest regression were trained on 5000 FEM samples per type, capturing key input–output relationships across geometric and material variables. These models enabled over 1000-fold acceleration in optimization time compared to full FEM runs, allowing GA-based design exploration across 40 generations with 200 individuals each. The optimized prosthesis designs favored low-density (1000–1600 kg/m 3 ) and moderately stiff (2–6 MPa) materials, confirming clinical preferences for cartilage. Geometries with smaller cross-sectional areas and tapered shapes consistently improved high-frequency sound transmission. The framework also revealed multimodal design landscapes, underscoring the importance of global search strategies. This work demonstrates a scalable, clinically relevant approach to prosthesis optimization that supports patient-specific customization and efficient design-space exploration, with potential to improve outcomes in otologic surgery.