Inverse characterization of radius bone using low-frequency ultrasonic guided waves
Bibliographic record
Abstract
The success of the cortical bone characterization using the axial transmission is highly dependent on the inversion model used to match experimental data with the true bone properties. Simplified models such as plate or cylinder are typically used in the literature. In our previous work, a more elaborate model based on a bone-like geometry built using semi-analytical finite element (SAFE) method was introduced. The model was successfully implemented in an inverse characterization routine using laboratory-controlled measurements on bone phantoms. Thus, the aim of this work is to apply the proposed inverse scheme on the characterization of ex-vivo radius samples. In order to do so, five radiuses were taken from donors aged between 53 and 88 years old and tested using typical axial transmission configuration. Ultrasonic guided wave modes were excited at low-frequency (100-400 kHz) using a piezoelectric transducer and measured using a laser interferometer at the middle of the diaphysis. The measured velocities were systematically compared to velocities obtained with the SAFE model in order to predict the cortical bone properties. For each sample, four parameters were estimated: (1) Young’s modulus; (2) density; (3) thickness; and (4) outer diameter. The results showed a notable correlation of the thickness and outer diameter with respect to the μCT images of the samples, while a less significant correlation was observed for the Young’s modulus and density with respect to the gray level of the μCT images.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".