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Updated: Aug 15, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
Published on: April 11, 2018
Patient-specific finite element modeling of scoliotic curve progression using region-specific stress-modulated
Christian R D'Andrea1, Amer F Samdani2, Sriram Balasubramanian3
1School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Bossone 718, Philadelphia, PA, 19104, USA.
Purpose:
This study describes the creation of patient-specific (PS) osteo-ligamentous finite element (FE) models of the spine, ribcage, and pelvis, simulation of up to three years of region-specific, stress-modulated growth, and validation of simulated curve progression with patient clinical angle measurements.
Research Question:
Does the inclusion of region-specific, stress-modulated vertebral growth, in addition to scaling based on age, weight, skeletal maturity, and spine flexibility allow for clinically accurate scoliotic curve progression prediction in patient-specific FE models of the spine, ribcage, and pelvis?
Methods:
Frontal, lateral, and lateral bending X-Rays of five AIS patients were obtained for approximately three-year timespans. PS-FE models were generated by morphing a normative template FE model with landmark points obtained from patient X-rays at the initial X-ray timepoint. Vertebral growth behavior and response to stress, as well as model material properties were made patient-specific based on several prognostic factors. Spine curvature angles from the PS-FE models were compared to the corresponding X-ray measurements.
Results:
Average FE model errors were 6.3 ± 4.6°, 12.2 ± 6.6°, 8.9 ± 7.7°, and 5.3 ± 3.4° for thoracic Cobb, lumbar Cobb, kyphosis, and lordosis angles, respectively. Average error in prediction of vertebral wedging at the apex and adjacent levels was 3.2 ± 2.2°. Vertebral column stress ranged from 0.11 MPa in tension to 0.79 MPa in compression.
Conclusion:
Integration of region-specific stress-modulated growth, as well as adjustment of growth and material properties based on patient-specific data yielded clinically useful prediction accuracy while maintaining physiological stress magnitudes. This framework can be further developed for PS surgical simulation.
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