Investigating Transducer–Tissue Interface Pressure for Soft Tissue Stress–Strain Behavior and the Effects on Echoic Intensities in Ultrasound Imaging of Periodontium
Bibliographic record
Abstract
Abstract Diagnostic ultrasound (US) is a major imaging modality to visualize soft tissues and blood flow with the advantages of real‐time imaging, high acceptability to patients, and absence of ionizing radiation. US imaging provides important clinical measurements, e.g., thickness of gingiva for treatment planning in orthodontics, periodontics, and implantology; or thickness of subcutaneous adipose for optimizing insulin injection. However, the image quality and measurements of anatomical structures can be inconsistent, i.e., due to varying pressure exerted by an US transducer. Herein, a simple device is developed to real‐time measure the interface pressure applied on tissues by the US transducer. A thin‐film piezo‐resistive sensor with a small footprint is integrated to sense the pressure. A theoretical model, based on hyperelastic material behavior, is verified using the pressure measured by the thin film sensor and the thickness determined on ultrasonograms. The device is also tested on porcine samples in the pressure range of 50–300 kPa for imaging gingiva boundaries, identifying tissue thickness, and probing tissue biomenchanical properties. The device enables the understanding on the optimal range of applied pressure for higher contrast imaging. The information of the on‐tissue pressure and the tissue deformation determined on the US images help to derive the biomechanical stress–strain behavior of the tissues.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".