Towards a shape-performance integrated digital twin for lumbar spine analysis

Background: With significant advancement and demand for digital transformation, the digital twin has been gaining increasing attention as it is capable of establishing real-time mapping between physical space and virtual space. In this work, a shape-performance integrated digital twin solution is presented to predict the real-time biomechanics of the lumbar spine during human movement. Methods: A finite element model (FEM) of the lumbar spine was firstly developed using computed tomography (CT) and constrained by the body movement which was calculated by the inverse kinematics algorithm. The Gaussian process regression was utilized to train the predicted results and create the digital twin of the lumbar spine in real-time. Finally, a three-dimensional virtual reality system was developed using Unity3D to display and record the real-time biomechanics performance of the lumbar spine during body movement. Results: The evaluation results presented an agreement (R-squared > 0.8) between the real-time prediction from digital twin and offline FEM prediction. Conclusions: This approach provides an effective method of real-time planning and warning in spine rehabilitation.