A nonlinear grid-search inversion for cortical bone thickness and ultrasonic velocities
Bibliographic record
Abstract
Cortical thickness and elasticity are important determinants of bone strength. This study proposes a nonlinear grid-search inversion to estimate the thickness and ultrasonic velocities of long cortical bones from axially-transmitted data. The inversion scheme has been developed in the frequency-phase velocity domain to recover bone properties. The method uses ultrasonic guided waves to retrieve cortical thickness, compressional, and shear-wave velocities of the cortex. The inversion strategy requires to systematically examine a set of trial dispersion curves within a pre-defined model space to match the data which minimizes the objective function in a least-squares sense. The theoretical dispersion curves required by the inversion are computed for bilayered bone models using a semi-analytical finite-element method. Its application demonstrates the feasibility of the proposed approach on synthetic data for a 5 mm-thick bone plate, and in-vitro dataset from a bovine femur plate with 2 mm-thick soft-tissue mimic on top. Our results indicate that one can recover the cortical thickness and wave speeds with less than 5% error. The accuracy can be improved by refining the grid; however, the computational cost of the method will increase. In the future, we aim at applying the method to clinical data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".