Numerical Characterization of Quasi-Static Ultrasound Elastography for the Detection of Deep Tissue Injuries
Bibliographic record
Abstract
Deep tissue injuries are subcutaneous regions of tissue breakdown associated with excessive mechanical pressure for extended period of time. These wounds are currently clinically undetectable in their early stages and result in severe burdens on not only the patients who suffer from them, but the health care system as well. The goal of this work was to numerically characterize the use of quasi-static ultrasound elastography for detecting formative and progressive deep tissue injuries. In order to numerically characterize the technique, finite-element models of sonographic B-mode imaging and tissue deformation were created. These models were fed into a local strain-estimation algorithm to determine the detection sensitivity of the technique on various parameters. Our work showed that quasi-static ultrasound elastography was able to detect and characterize deep tissue injuries over a range of lesion parameters. Simulations were validated using a physical phantom model. This work represents a step along the path to developing a clinically relevant technique for detecting and diagnosing early deep tissue injuries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".