A novel methodology to estimate bone mechanical properties using dual-energy imaging to improve pedicle screw fixation
Bibliographic record
Abstract
IntroductionVertebral bone is composed of trabecular and cortical bone tissue, providing the vertebral body a structural resistance to different types of loads withheld throughout a lifetime.Trabecular bone has been shown to be distributed heterogeneously within the vertebral body, where some regions are denser than others like the pedicle area 1 , to provide such resistance.The constant change of loads accords the bone an anisotropic behaviour, consequently altering its mechanical properties 2 .Therefore, the bone mineral density (BMD) varies considerably between certain regions in the vertebral body 3,4 , between vertebral levels 5 , and even between individuals according to their age 6,7 , as the evolution of bone density degradation leads for example to osteoporosis.Several studies have reported mathematical relationships to relate BMD and mechanical properties of interest that act on the vertebrae, such as the Young's modulus 3,[8][9][10][11] .These relationships give an insight into better understanding the macro-and micro-structural behaviour of trabecular bone.Such understanding is particularly valuable to improve methods of screw fixation in spine surgery.During spine surgery, instrumentation is needed to correct a deformity or treat a fracture where contoured rods are connected to pedicle screws.Pedicle screws need a proper purchase to sustain the loads exerted to correct Abstract Objective: To develop a methodology to improve the representation of the mechanical properties of a vertebral finite element model (FEM) based on a new dual-energy (DE) imaging technology to improve pedicle screw fixation.Methods: Bone-calibrated radiographs were generated with dual-energy imaging technology in order to estimate the mechanical properties of the trabecular bone.Properties were included in regions of interest in four vertebral FEMs representing heterogeneity and homogeneity, as a realistic and reference model, respectively.Biomechanical parameters were measured during screw pull-out testing to evaluate pedicle screw fixation.Results: Simulations with property distributions deduced from dual-energy imaging characterization (heterogeneous models) induced an increase in biomechanical indicators versus with a homogeneous representation, implying different behaviors for the subject-specific models.Conclusion: The presented methodology allows a patient-specific representation of bone quality in a FEM using new DE imaging technology.Consideration of individualized bone distribution in a spinal FEM improves the perspective of orthopedic surgical planning over otherwise underestimated results using a homogeneous representation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".