On the potential of ultrasound elastography for pressure ulcer early detection
Bibliographic record
Abstract
PURPOSE: Pressure ulcers are areas of soft tissue breakdown induced by a sustained mechanical stress that damages the skin and underlying tissues. They represent a considerable burden to the society in terms of health care and cost. Yet, techniques for prevention and detection of pressure ulcers still remain very limited. In this article, the authors investigated the potential of ultrasound elastography for pressure ulcer early detection. Elastography is an imaging technique providing local information on biological tissue mechanical properties. It is relevant for pressure ulcer detection as this pathology is associated with a gradual stiffening of damaged tissues, beginning in the deeper tissues and progressing toward the skin surface. METHODS: A 2D ultrasound elastography method was proposed and its ability in terms of pressure ulcer detection was validated through numerical simulations and physical acquisitions on pressure ulcer mimicking phantoms. In vivo experiments on a rat model are also reported. A maintained pressure was applied on the animal thigh, with a view to generate a pressure ulcer, and ultrasound data were acquired and processed before and after application of this pressure. RESULTS: Numerical simulations demonstrated that a pressure ulcer can theoretically be detected at a very early stage with ultrasound elastography. Even when the ulcer region was characterized by a low stiffening (ratio of 1.8 relative to normal tissues), the corresponding elastogram clearly underlined the pathological area. This observation was confirmed by the results obtained on a physical phantom mimicking a pressure ulcer at an early stage. Computed elastograms showed strain differences between areas mimicking healthy and pathological tissues. Results corresponding to in vivo experiments revealed a difference in the way tissues behaved before and after the pressure was applied on the animal thigh, which strongly suggests the presence of a pathological area. CONCLUSIONS: Experiments demonstrated that ultrasound elastography is a promising technique for pressure ulcer detection, especially at an early stage of the pathology, when the disease is still visually undetectable. In the absence of any gold standard method, this is also a first step toward the development of a quantitative technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".