Prediction of pedicle screw fixation strength under craniocaudal cyclic load: comparison of various models trained on quantitative CT based finite element analysis.

PURPOSE: This study aims to predict FEA-derived screw fixation strength (FS-CL) under craniocaudal cyclic load using machine learning and deep learning, and to explore whether FS-CL can serve as a surrogate marker for pedicle screw loosening (PSL) risk.

METHOD: A retrospective analysis was conducted on 618 screw trajectories data from preoperative of 112 patients. Various ML and DL models utilizing CT images and screw trajectory, were developed to predict screw FS-CL including multilayer perceptron (MLP) and dual-channel 3D ResNet-18 models. Model performance was evaluated using mean squared error (MSE), coefficient of determination (R²) on an external validation set of 126 trajectories. Additionally, we validated the clinical efficiency of the model for the risk assessment of PSL based on a case-control cohort of 62 patients.

RESULT: The MLP and 3D ResNet-18 models demonstrated reliable FS-CL predictions, with less time spent compared to the manual FEA. All DL and ML model that focused on region surrounding screw trajectory performed better. The ResNet-18 model achieved the highest predictive performance for screw FS-CL (MSE: 0.009, R²: 0.836) and highest prediction for PSL risk with an AUC value of 0.826. The MLP model also exhibited moderate performance, outperforming other ML models.

CONCLUSION

AI models proposed in this study can accurately predict FEA-derived FS-CL efficiently providing a supplementary tool for PSL risk evaluation.