Predictive Biomechanical Study on the Human Cervical Spine Under Complex Physiological Loading

This study aims to predict the range of motion (ROM) with secondary parameters such as the intra-disc pressure (IDP) and facet force under complex physiological loading for Anterior Cervical Discectomy and Fusion (ACDF) and degeneration occurring at various functional spine units (FSU) in a human cervical spine, using machine learning models. Multi-target regression is a machine learning (ML) algorithm that outputs an array of values for a given set of input parameters. An anatomically accurate and validated finite element model (FEM) of a human sub-axial spinal column (C2-T1) was used in this study. Material properties for all spine components were taken from literature. An algorithm programmed using Python was interfaced with ABAQUS to automate the calculation of ROM, IDP and facet force generated from the nodal and elemental data of an intact model, a model with ACDF at the C5-C6 level and a model with mild degeneration at C5-C6 separately. The data generated from the FEA results were trained with random forest regression, support vector machines and multiple linear regression algorithms. The results indicated that the R² value was significantly high for the random forest regression model, accounting for a very less Root Mean Square Error (RMSE) and was able to predict more than one target variable unlike the rest. Conclusively, the target variables were predicted under complex loading conditions for clinical conditions of fusion and degeneration with high accuracy and less computational cost.