ArticleJournal of orthopaedic surgery and research2025
Patient-specific spine digital twins: a computational characterization of the idiopathic scoliosis.
Article in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Coronal curvature angle and asymmetrical blood vessel distribution in intervertebral discs from aged cadavers: a pilot study.Spine deformity · 2026Article
- From Concept to Clinic: Pre-Clinical Testing and Regulatory Considerations in Spine Implant Development.JOR spine · 2026Review
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
- Main elements of current spine biomechanics research: model, installation and test data.Frontiers in bioengineering and biotechnology · 2025Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundScoliosis is an idiopathic three-dimensional spine strain. The orthopedic parameter used to diagnose and evaluate the severity of the strain is Cobb's angle. This study proposes using this clinical parameter to reproduce a digital twin of the spine, calculate biomechanical stress changes, and characterize idiopathic scoliosis deformity through symmetrical degeneration of intervertebral discs, relying on patient-specific radiological measurements of the scoliotic curves.
methodsA three-dimensional computational model of the spine was developed, where patient-specific curves were generated by modifying intervertebral disc mechanical properties via a mathematical model derived from radiological data. Validation of the model was performed by comparing the resultant scoliotic curves with patient radiological images. Finite element analysis was then used to elucidate the biomechanical effects on the spine due to the deformity.
resultsThe model successfully replicated patient-specific thoracic scoliotic deformities, revealing a discernible relationship between disc strain and its proximity to the apex, indicating a heightened risk of disc stress closer to the apex. Moreover, "type-C" curves exhibited a greater risk of herniation compared to "type-S" curves due to differences in compressive stress distribution.
conclusionThis modeling approach enhances the understanding of scoliosis biomechanics, facilitating risk assessment for disc prolapse and aiding in treatment selection, including the design of condition-specific orthotics. Furthermore, it establishes a quantitative link between scoliosis severity and disc strain, integrating Cobb's angle and other orthopedic parameters into computational models to approximate patient-specific conditions.
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