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

Featured Application Prediction of orthopedic implant stability, particularly pedicle screws, as an index comparable to insertion torque. 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 R-2 = -0.144 to R-2 = 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.

Automated summary

This study aims to predict the stability of orthopedic implant, specifically pedicle screws, by comparing it to the insertion torque. Laser resonance frequency analysis (L-RFA) is used to predict the insertion torque of pedicle screws placed in cadaveric bone. Machine learning analysis is optimized using a dataset with artificial bone, and combining artificial and cadaveric bone data improves prediction accuracy and reduces the influence of bone differences.