Implementation of a Burn Scar Assessment System by Ultrasound Techniques
Bibliographic record
Abstract
Tissue injury and its ensuing healing process cause scar formation. In addition to physical disability, the subsequent disfigurements from burns often bring negative psychological impacts on the survivors. Scar hypertrophy and contracture limit the joint motion and body function of the patient. With fast development of the current available technologies regarding the scar therapies, not only the process of wound healing has to be focused, but also the cosmetic and functional outcomes need to be emphasized. Therefore, proper evaluation and assessment of the healing process to nil scar status is highly recommended. However, the currently employed tools for scar evaluation are mostly subjective. For example, Vancouver General Hospital (VGH) scar index uses color, pigmentation, vascularity, pliability, and depth of the scar as dependent variables for scar evaluation. These parameters only estimate the superficial surface of the scar, but they can not evaluate the deeper tissue within dermis. Ultrasound is a safe, inexpensive, and multifunctional technique for probing tissue characteristics. In addition, its resolution is not inferior to other measurement techniques. Although 3D-ultrasound is available in clinical application, it's still not widely used in scar evaluation because of its high cost. In this study, we proposed a system for scar assessment using B-mode ultrasonic technique. By utilizing the reconstruction methods to search the scar border, many characteristic parameters, including depth, area and volume, can be estimated. The proposed method is useful in assisting the clinician to evaluate the treatment effect and to plan further therapeutic strategy more objectively. In this report, the quantitative assessment system was used to evaluate the scar of a seriously burned patient. In order to verify the reliability of systematic reconstruction method, we constructed a phantom to imitate the scar tissue. The results show that it can achieve more than 90% in accuracy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".