Constructing anisotropic finite element model of bone from computed tomography (CT)
Bibliographic record
Abstract
Image-based finite element (FE) modeling of human bones has been increasingly applied as a useful tool in biomedical engineering. However, most existing image-based FE models assume isotropic mechanical properties for bones, although bones are typically anisotropic material. In this study, we attempted to construct anisotropic FE models from medical computed tomography (CT) scans by modifying the existing empirical relations of bone elasticity-density. The hypothesis adopted in the study is that bone anisotropy is generated by the variations of bone density and the proposed anisotropic relations should degenerate to the isotropic ones if bone density variation is taken zero. The effect of considering bone anisotropy in FE models was investigated by numerical studies. The obtained numerical results showed that the relative error in the finite element solutions produced respectively by the isotropic and anisotropic FE models can be as large as 50%. We concluded from this preliminary study that the consideration of anisotropy in bone FE models has a significant effect on the accuracy of bone behavior predicted by the FE models. However, well-designed bone tests have to be conducted to validate the anisotropic bone elasticity-density relation proposed in this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".