Pullout Strength Predictor: A Machine Learning Approach

Abstract

Study DesignA biomechanical study.PurposeTo develop a predictive model for pullout strength.Overview of LiteratureSpine 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.MethodsTo 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.ResultsRandom 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).ConclusionsThis model can be used clinically for understanding the failure of pedicle screw pullout and pre-surgical planning for spine surgeon.

Stress distribution of different lumbar posterior pedicle screw insertion techniques: a combination study of finite element analysis and biomechanical test

“…With more in-depth studies, pedicle screw loosening has been proved to closely related to the decrease of pullout strength and the change of insertion torque. The main influencing factors of pullout strength are density, insertion angle, insertion depth and reinsertion 33 . And bone density of vertebra is an important factor in pedicle screw instrumentation and contributes highest toward insertion torque (82%) and pull out strength (76%) 34 .…”

Exoscope-assisted spine surgery: Current applications and future directions

“…The proposal to use an exoscope must be discussed preoperatively, with assurance that patients have the capacity to make informed decisions and provide consent. 23 Moreover, despite the scarcity of literature on exoscope-assisted spine surgery and the pressing need for research to fully assess its effectiveness, maintaining the security of patient data is of critical importance, requiring strict adherence to ethical guidelines.…”

The application of artificial intelligence in spine surgery

“…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. Similarly, in 2019, Khatri et al ( 49 ) used an experimental dataset of 48 data points as training data to construct a model based on different machine learning algorithms. They also used the L9 orthogonal array of Taguchi Design of Experiments to obtain the best combination of parameters for predicting the pullout strength.…”