Predicting cervico-thoraco-lumbar vertebra positions from cutaneous markers: Combining local frame and postural predictors improves robustness to posture.

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.