Asian Spine J.  2019 Oct;13(5):842-848. 10.31616/asj.2018.0243.

Pullout Strength Predictor: A Machine Learning Approach

• Khatri R
• Varghese V
• Sharma S
• Kumar GS
• Chhabra HS

• KMID: 2462015
• DOI: http://doi.org/10.31616/asj.2018.0243

Abstract

STUDY DESIGN: A biomechanical study. PURPOSE: To develop a predictive model for pullout strength. OVERVIEW OF LITERATURE: Spine fusion surgeries are performed to correct joint deformities by restricting motion between two or more unstable vertebrae. The pedicle screw provides a corrective force to the unstable spinal segment and arrests motions at the unit that are being fused. To determine the hold of a screw, surgeons depend on a subjective perioperative feeling of insertion torque. The objective of the paper was to develop a machine learning based model using density of foam, insertion angle, insertion depth, and reinsertion to predict the pullout strength of pedicle screw.
METHODS
To predict the pullout strength of pedicle screw, an experimental dataset of 48 data points was used as training data to construct a model based on different machine learning algorithms. A total of five algorithms were tested in the Weka environment and the performance was evaluated based on correlation coefficient and error matrix. A sensitive study of various parameters for obtaining the best combination of parameters for predicting the pullout strength was also preformed using the L9 orthogonal array of Taguchi Design of Experiments.
RESULTS
Random forest performed the best with a correlation coefficient of 0.96, relative absolute error of 0.28, and root relative squared error of 0.29. The difference between the experimental and predicted value for the six test cases was not significant (p >0.05).
CONCLUSIONS
This model can be used clinically for understanding the failure of pedicle screw pullout and pre-surgical planning for spine surgeon.

Keyword

Pedicle screw; Machine learning; Osteoporosis; Decision support; Pullout; Polyurethane foam; Implant; Spine; Spine fusion

MeSH Terms

Congenital Abnormalities
Dataset
Forests
Joints
Machine Learning*
Osteoporosis
Pedicle Screws
Spine
Surgeons
Torque