Laser Speckle Contrast Imaging for the Objective Assessment of Blood Perfusion in Keloids Treated With Dual-Wavelength Laser Therapy
Bibliographic record
Abstract
BACKGROUND: Most of the widely used methods for the assessment of keloid treatment are subjective grading scales based on the opinion of an individual clinician or patient. There is a growing need for objective methods to evaluate keloid treatment. OBJECTIVE: This study aimed to evaluate the value of laser speckle contrast imaging (LSCI) as an objective method for the assessment of dual-wavelength laser therapy for keloids. METHODS: This prospective study included 21 patients with 54 keloids. All patients were treated with a combined 585-nm pulsed dye laser and 1,064 nm neodymium-doped yttrium aluminum garnet dual-wavelength laser at 4 weeks to 6 weeks intervals. Keloids were assessed using the Vancouver Scar Scale (VSS) and LSCI. RESULTS: The total VSS score significantly decreased after 4 sessions of treatment (p < .05). Blood perfusion in keloids as measured by LSCI was significantly reduced after treatment (p < .05). The improvement of chest keloids in terms of the total VSS score and blood perfusion was significantly greater than that of scapular keloids (p < .05). There was a positive correlation between decreased perfusion and reduced total VSS score (R2 = 0.84). CONCLUSION: Blood perfusion in keloids significantly decreased after dual-wavelength laser therapy. Laser speckle contrast imaging is a promising objective method for assessing the improvement of keloids treated with laser therapy.
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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) | 0.000 | 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 | 0.000 | 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".