Optimization of primary screw stability in Trabecular bone using neural network-based models

Abstract

Background and Objective

Screw implant stability in bone is crucial to the success of many orthopaedic procedures, yet the relationship between screw design parameters and specific bone characteristics remains underexplored. This study aims to optimize screw designs to enhance primary stability by leveraging subject-specific bone data and advanced surrogate modelling techniques.

Methods

In this study, 2880 screw pull-out simulations were conducted to assess primary screw stability by analysing pull-out stiffness and strength. The resulting dataset was used to develop surrogate models using multiple linear regression, random forest, and neural networks (NN). An optimization process was then applied to find optimal screw designs for 80 distinct trabecular bone specimens, in terms of inner diameter, pitch, and thread angle.

Results

The models, trained with various input parameters, including bone morphological parameters and computed tomography images, promisingly predicted the results of the simulations. The prediction errors varied by model type, with multiple linear regression yielding approximately 12 % error, while non-linear machine learning models achieved lower errors, ranging between 2–6 %. The series of subsequent optimization tasks provided optimized screw designs showing statistically significant improvements in pull-out stiffness and strength compared to the average screw designs (approximately 16 and 14 %, respectively). This even though our study focused only on screw design parameters that generally have a smaller impact on stability compared to factors such as screw outer diameter and insertion depth.

Conclusions

Multiple linear regression models were found to be insufficient for generating optimized screw configurations, and more complex surrogate models, such as NN, are needed. It could be concluded that different trabecular bone morphologies can benefit from distinct optimal screw designs. The insights gained from this study could have implications for the development of patient-specific orthopaedic treatments.