Background: This work uses genetic algorithm (GA) for optimum design of patient
specific spinal implants (pedicle screw) with varying implant diameter and bone condition. The optimum
pedicle screw fixation in terms of implant diameter is on the basis of minimum strain difference
from intact (natural) to implantation at peri-prosthetic bone for the considered six different
Methods: This design problem is expressed as an optimization problem using the desirability function,
where the data generated by finite element analysis is converted into an artificial neural network
(ANN) model. The finite element model is generated from CT scan data. Thereafter all the
ANN predictions of the microstrain in six positions are converted to unitless desirability value varying
between 0 and 1, which is then combined to form the composite desirability. Maximization of
the composite desirability is done using GA where composite desirability should be made to go up
as close as possible to 1. If the composite desirability is 1, then all ‘strain difference values in 6 positions’
Results: The optimum solutions obtained can easily be used for making patient-specific spinal implants.
Keywords: Spinal implant, pedicle screw, microstrain, design, modeling, optimization, finite element analysis, artificial neural
network, genetic algorithm, composite desirability.
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