Evaluating Pedicle-Screw Instrumentation Using Decision-Tree Analysis Based on Pullout Strength

Varghese, Vicky; Krishnan, Venkatesh; Kumar, Gurunathan Saravana

Abstract

STUDY DESIGN: A biomechanical study of pedicle-screw pullout strength. PURPOSE: To develop a decision tree based on pullout strength for evaluating pedicle-screw instrumentation. OVERVIEW OF LITERATURE: Clinically, a surgeon's understanding of the holding power of a pedicle screw is based on perioperative intuition (which is like insertion torque) while inserting the screw. This is a subjective feeling that depends on the skill and experience of the surgeon. With the advent of robotic surgery, there is an urgent need for the creation of a patient-specific surgical planning system. A learning-based predictive model is needed to understand the sensitivity of pedicle-screw holding power to various factors.
METHODS
Pullout studies were carried out on rigid polyurethane foam, representing extremely osteoporotic to normal bone for different insertion depths and angles of a pedicle screw. The results of these experimental studies were used to build a pullout-strength predictor and a decision tree using a machine-learning approach.
RESULTS
Based on analysis of variance, it was found that all the factors under study had a significant effect (p <0.05) on the holding power of a pedicle screw. Of the various machine-learning techniques, the random forest regression model performed well in predicting the pullout strength and in creating a decision tree. Performance was evaluated, and a correlation coefficient of 0.99 was obtained between the observed and predicted values. The mean and standard deviation of the normalized predicted pullout strength for the confirmation experiment using the current model was 1.01±0.04.
CONCLUSIONS
The random forest regression model was used to build a pullout-strength predictor and decision tree. The model was able to predict the holding power of a pedicle screw for any combination of density, insertion depth, and insertion angle for the chosen range. The decision-tree model can be applied in patient-specific surgical planning and a decision-support system for spine-fusion surgery.