Validation of 3D skin imaging for objective repeatable quantification of severity of atrophic acne scarring
Bibliographic record
Abstract
Abstract Background One major sequelae of acne is atrophic scarring, yet objective tools to assess scars are lacking. Neither depth nor volume of atrophic scars is readily evaluable clinically and standard 2D photography is significantly affected by lighting and shadows. The aim of our study was to define and evaluate parameters of 3D imaging that can be used to assess severity of atrophic acne scarring. Methods Single center study of 31 patients with acne scarring. A target area of 3 × 3 cm was defined on the face. The global severity of atrophic acne scarring in the target area was evaluated by 5 dermatologists and scars were counted and categorized by size (scars < 2 mm, 2‐4 mm, and > 4 mm in diameter). Three dimensional images of the target area were acquired with the LifeViz Micro® system and analysis was performed using MountainsMaps® software. An algorithm was developed to quantify the scar volume loss: shape removal step, with an order 5 polynomial, and to calculate the Valley void volume 80% (Vvv 80%) defined in the ISO‐25178 standard for 3D surface texture. Results Correlation coefficient of the Vvv parameter to mean global severity at the target area rating was 0.77. The volume of scars evaluated with the Vvv parameter was mainly impacted by scars > 2 mm. The evaluations demonstrated good repeatability (with an intra‐class correlation coefficient ICC = 0.98). Conclusions We demonstrate convergent validation to clinical assessment and repeatability of 3D skin imaging in atrophic acne scarring. Image analysis is straightforward and can be integrated into an automated workflow.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 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".