Multiobjective optimization design of spinal pedicle screws using neural networks and genetic algorithm: mathematical...

Area of Science:

• Spine surgery
• Biomechanical engineering
• Medical device design

Background:

• Short-segment spinal instrumentation failure rates are high, often due to pedicle screw breakage or loosening.
• Conflicting design objectives, such as bending and pullout strength, complicate optimal screw design.
• Existing pedicle screw designs may not adequately address the trade-offs between critical mechanical properties.

Purpose of the Study:

• To perform a multiobjective optimization study on spinal pedicle screw design.
• To develop and validate a computational model for predicting screw performance.
• To identify optimal screw designs balancing bending and pullout strength.

Main Methods:

• Utilized three-dimensional finite element (FE) analysis with an L25 orthogonal array.
• Developed objective functions for bending and pullout strength using an artificial neural network (ANN) algorithm.
• Explored trade-off solutions (Pareto optima) using a genetic algorithm (GA).

Main Results:

• The study identified 'knee' solutions on the Pareto fronts offering high bending (92-94%) and pullout strength (92-94%) of their maxima.
• Mathematical analysis results closely correlated with experimental tests (R = -0.91 for bending, R = 0.93 for pullout).
• The optimized design demonstrated significantly higher fatigue life and comparable pullout strength to commercial screws.

Conclusions:

• A hybrid ANN and GA approach enables simultaneous optimization of bending and pullout strength in spinal pedicle screws.
• This multiobjective optimization can lead to improved spinal instrumentation with enhanced fixation integrity.
• The findings provide a pathway for developing superior pedicle screw designs for treating spinal fractures.