Handheld SFDI/polarimetric imaging device for objective evaluation of hypertrophic scars (Conference Presentation)
Bibliographic record
Abstract
Scars can be debilitating and cause serious functional limitations, significantly reduced physical function and loss of ability to perform normal daily activities. Scar formation is not fully understood and the treatment options have been hampered by the lack of an objective diagnostic tool to assess scars. Presently, assessment of hypertrophic scars has been based on subjective clinician rankings using a four-parameter scale called the Vancouver Scar Scale (VSS) or the Patient Observer Scar Assessment Scale (POSAS) but no objective, standardized tool for quantifying scar severity is available, despite known inadequacies of the subjective scales. We have developed a hand-held multi modal system consisting of a combined Spatial Frequency Domain Imager (SFDI) used for the assessment of tissue molecular components and a polarimeter for structural measurements. The SFDI capability is provided by an Arduino board controlled spectrally and polarimetric diverse Light Emitting Diodes (LED) ring illuminator. For SFDI imagery, the LEDs are combined with sinusoidal patterns. A single pattern snapshot SFDI approach is used to observe and quantify the biological components in the scar tissue including: oxygenated and de oxygenated hemoglobin, water, and melanin. The SFDI system is integrated with a reduced Mueller Matrix polarimetric system, whose illumination is also included in the LED’s ring, and providing for the assessment of collagen orientation through Mueller Matrix decomposition. The design of the system and experimental work on phantoms will be presented.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".