Improvement of Machine Learning-Based Prediction of Pedicle Screw Stability in Laser Resonance Frequency Analysis via Data Augmentation from Micro-CT Images

Abstract

To prevent pedicle screw implant failure, a diagnostic technique that allows surgeons to evaluate implant stability easily, quickly, and quantitatively in clinical orthopedic situations is required. This study aimed to predict the insertion torque equivalent to laboratory-level evaluation accuracy. This serves as an index of the implant stability of pedicle screws placed in cadaveric bone, which relies on laser resonance frequency analyses (L-RFA) when irradiating with two types of lasers. The machine learning analysis was optimized using a dataset with artificial bone as teaching data. In this analysis, many explanatory variables extracted from the laser-induced vibration spectra obtained during an analysis/RFA evaluation were predicted by selecting important variables using the least absolute shrinkage and selection operator and performing a non-linear approximation using support vector regression. It was found that combining both artificial and cadaveric bone data with the bone densities as teaching data dramatically improved the determination coefficient from R2 = −0.144 to R2 = 0.858 as the prediction accuracy and reduced the influence of differences between artificial and cadaveric bones. This technology will contribute to the development of preventive diagnostic technologies that can be used during surgery, which is necessary in order to further advance treatment technologies.