The present study proposes a semiautomatic software approach to reconstruct 3D subject-specific musculoskeletal model of thoracolumbar spine from radiographic digitized images acquired with EOS system. The approach is applied to evaluate the intervertebral loads in 38 standing adolescents with mild idiopathic scoliosis. For each vertebra, a set of landmarks was manually identified on radiographic images. The landmark coordinates were processed to calculate the following vertebral geometrical properties in the 3D space (i) location (ii) dimensions; and (iii) rotations. Spherical joints simulated disks, ligaments, and facet joints. Body weight distribution, muscles forces, and insertion points were placed according to physiological-anatomical values. Inverse static analysis, calculating joints' reactions in maintaining assigned spine configuration, was performed with AnyBody software. Reaction forces were computed to quantify intervertebral loads, and correlation with the patient anatomical parameters was then checked. Preliminary validation was performed comparing the model outcomes with that obtained from other authors in previous modeling works and from in vivo measurements. The comparison with previous modeling works and in vivo studies partially fulfilled the preliminary validation purpose. However, minor incongruities were pointed out that need further investigations. The subjects' intervertebral loads were found significantly correlated with the anatomical parameters in the sagittal and axial planes. Despite preliminary encouraging results that support model suitability, future investigations to consolidate the proposed approach are necessary. Nonetheless, the present method appears to be a promising tool that once fully validated could allow the subject-specific non-invasive evaluation of a deformed spine, providing supplementary information to the routine clinical examination and surgical intervention planning.
Keywords: 3D model reconstruction; musculoskeletal modeling; scoliosis; spine biomechanics; spine loading prediction.