ABSTRACT
Predictions of vertebra positions from external data are required in many fields like motion analysis or for clinical applications. Existing predictions mainly cover the thoraco-lumbar spine, in one posture. The objective of this study was to develop a method offering robust vertebra position predictions in different postures for the whole spine, in the sagittal plane. EOS radiographs were taken in three postures: slouched, erect, and subject's usual sitting posture, using 21 healthy participants pre-equipped with opaque cutaneous markers. Local curvilinear Frenet frames were built on a spline fitted to spinous processes' cutaneous markers. Vertebra positions were expressed as polar coordinates in these frames, defining an angle (α) and distance (d). Multilinear regressions were fitted to explain α and d from anthropometric predictors and predictors presumed to be linked to spinal posture, the predictors' effects being considered both locally and remotely. Anthropometric predictors were the main predictors for d distances, and postural predictors for α angles, with postural predictors still showing a marked influence on d distances for the cervical spine. Vertebra positions were then predicted by cross-validation. The average RMSE on vertebra positions was 11.0 ± 3.7 mm across the entire spine, 13.4 ± 4.1 mm across the cervical spine and 10.1 ± 3.1 mm across the thoraco-lumbar spine for all participants and postures, performances similar to previous models designed for a single posture. Our simple geometrical and statistical model thus appears promising for predicting vertebra positions from external data in several spinal postures and for the whole spine.
