Biomechanical Models of the Human Thoracic and Lumbar Spine: a Statistical Approach for Prediction of Anatomical Parameters from Radiographic Images

Severe cases of scoliosis are treated using implants. The effects of different types of implants have been investigated with mathematical models with modifiable geometry. Our aim was perform a nonlinear regression analyses with anatomical data to generate prediction equations for vertebral and intervertebral disc dimensions as a function of only one given dimension measurable by X-ray, the vertebral body height. Third-order polyno-mial regressions provided moderate to high correlation between the vertebral body heights and the endplates and spinal canal; pedicle heights and the spinous process, in addition to a reasonable correlation of the posterior vertebral structures (pedicle and facet). A set of 50 equations was generated for the prediction of the spine dimensions based on the radiographic measurement of the vertebral body height. It was possible to establish useful predictions for all investigated dimensions. This is an efficient approach for obtaining anatomical data for modeling of the human thoracic and lumbar from measurement of only one parameter per vertebra without the need for direct measurement or 3D reconstructions from medical images.