Cortical bone properties assessment using axially transmitted low frequency (<500 kHz) guided waves
Bibliographic record
Abstract
The early diagnosis of osteoporosis through bone quality assessment has been extensively studied in the past decade. Research in axial transmission using ultrasonic guided waves has shown the method to be sensitive to intrinsic properties of long cortical bone. The aim of this work is, therefore, to show the capability of low frequency guided waves to enable the inversion of dispersion curves into bone properties. The proposed inversion scheme relies on dispersion curves simulated using the semi-analytical iso-geometric analysis (SAIGA) method. The model used in simulation comprised a bone phantom plate with a layer of soft tissue attached to the top surface in accordance with experimental bone phantom plates. A proprietary axial transmission multielement ultrasonic transducer specifically designed to excite ultrasonic guided waves under 500 kHz was used for measurements. Acquired data were processed using the 2-D Fast Fourier Transform to extract dispersion curves. The mechanical properties of the bone phantom plates were obtained by minimizing the difference between the experimental and simulated dispersion curves. The inversion was based on the dispersive properties of ultrasonic guided waves as well as their amplitudes. Results show a difference around 5% between the mechanical properties found with the SAIGA based inversion and those provided by the manufacturer of the plates.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".