ArticlePloS one2022
Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Beyond SINS: A Critical Review of Biomechanical, Microstructural, and Radiomic Biomarkers for Predicting Fracture Risk in Spinal Metastases.Diagnostics (Basel, Switzerland) · 2026Review
- Optimization of Bone Cement Stiffness in Metastatic Vertebral Augmentation: Balancing Strength Restoration and Stress Redistribution.Annals of biomedical engineering · 2026Article
- Deep learning segmentation with uncertainty quantification for spinal tuberculosis on fat-suppressed T2-weighted MRI.Frontiers in physiology · 2026Article
- Advances in biomechanical modeling of lumbar spine diseases and tumors: gaps, opportunities, and AI integration.Frontiers in bioengineering and biotechnology · 2026Review
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
- Can Multi-Vertebral CT-Based Finite Element Models Accurately Predict Strains? An In Vitro Validation Study.International journal for numerical methods in biomedical engineering · 2025Article
- Comparing the predictions of CT-based subject-specific finite element models of human metastatic vertebrae with digital volume correlation measurements.Biomechanics and modeling in mechanobiology · 2025Article
- Combined Rigid-Flexible Multibody Analysis Reveals Reduced Pedicle Screw Loads in Short-Segment Fixation for Decompressed Lumbar Spine Stabilization.Annals of biomedical engineering · 2025Article
- Loading Pressure Induced by 4 mm Implants on the Inferior Alveolar Nerve: A 3D Finite Element Analysis Model.Journal of clinical medicine · 2025Article
- Subject-Specific Geometry of FE Lumbar Spine Models for the Replication of Fracture Locations Using Dynamic Drop Tests.Annals of biomedical engineering · 2024Article
- Phantomless calibration of CT scans for hip fracture risk prediction in silico: Comparison with phantom-based calibration.PloS one · 2024Article
- Identification of a lumped-parameter model of the intervertebral joint from experimental data.Frontiers in bioengineering and biotechnology · 2024Article
- Reproducibility in modeling and simulation of the knee: Academic, industry, and regulatory perspectives.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2023Review
- Cross-link augmentation enhances CFR-PEEK short fixation in lumbar metastasis stabilization.Frontiers in bioengineering and biotechnology · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathologies such as cancer metastasis and osteoporosis strongly affect the mechanical properties of the vertebral bone and increase the risk of fragility fractures. The prediction of the fracture risk with a patient-specific model, directly generated from the diagnostic images of the patient, could help the clinician in the choice of the correct therapy to follow. But before such models can be used to support any clinical decision, their credibility must be demonstrated through verification, validation, and uncertainty quantification. In this study we describe a procedure for the generation of such patient-specific finite element models and present a first validation of the kinematics of the spine segment. Quantitative computed tomography images of a cadaveric lumbar spine segment presenting vertebral metastatic lesions were used to generate the model. The applied boundary conditions replicated a specific experimental test where the spine segment was loaded in compression-flexion. Model predictions in terms of vertebral surface displacements were compared against the full-field experimental displacements measured with Digital Image Correlation. A good agreement was obtained from the local comparison between experimental data and simulation results (R2 > 0.9 and RMSE% <8%). In conclusion, this work demonstrates the possibility to apply the developed modelling pipeline to predict the displacement field of human spine segment under physiological loading conditions, which is a first fundamental step in the credibility assessment of these clinical decision-support technology.
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