Competitive Hopfield neural network model for evaluating pedicle screw placement accuracy

Abstract

In this paper, the application of an X-ray image segmentation algorithm based on a Competitive Hopfield Neural Network (CHNN) model for evaluating the insertion accuracy of pedicle screws is presented. In practice, the evaluation of pedicle screw insertion accuracy is made visually in two planes and is based on postoperative computer tomography scans or radiography. In order to increase the reliability of the assessment, this research proposes a new approach that automates this process and can be used for developing a training system for pedicle screw implantation. The proposed approach implements a training method which allows extracting features of the pedicle screw from X-ray images segmented using a modified HNN algorithm, and compares them with values from a knowledge database. © 2012 Journal of Mechanical Engineering. All rights reserved.