Generación automatizada de modelos espinales específicos del sujeto basados en imágenes para la deformidad espinal...

Adult spinal deformity (ASD) involves complex three-dimensional (3D) spinal malalignments that impair mobility and stability. Current clinical assessments rely on static, two-dimensional (2D) radiographs, which fail to capture the 3D dynamics essential for comprehensive evaluations. While musculoskeletal models with marker-based motion analysis offer insights into kinematics, generic models fail to replicate the 3D deformities in ASD. This study introduces an automated workflow to generate image-based subject-specific models, capturing individual spinal geometry and alignment to enable analysis of 3D dynamics in patients with ASD.

A retrospective dataset of 13 deformity subjects was used to develop and evaluate the workflow. Spinopelvic bones were automatically segmented, followed by spinal joint and alignment definition. The accuracy of 3D spinal alignment was validated by simulating upright standing and bending positions as captured with biplanar radiography. 3D position and rotation differences were calculated against biplanar imaging-based reference markers.

3D position differences across spinal markers averaged 2.2 ± 1.6 mm in the upright, and 3.0 ± 1.9 mm in the bending poses. In bending simulations, differences were comparable to Overbergh et al. (2020) who achieved mean errors 3.0 ± 2.0 mm. 3D rotation differences averaged 3.5 ± 1.7° in the upright, and 5.3 ± 2.6° in the bending poses. The rotation differences in bending compared well with the method of Overbergh et al. (2020) being 5.1 ± 3.0° on average.

The proposed workflow enabled creation of image-based subject-specific models of patients with ASD, with anatomically correct spinopelvic bone geometries, intervertebral joints, and 3D alignment.