Computerized morphometry and three-dimensional image reconstruction in the evaluation of scalp biopsy from patients with non-cicatricial alopecias
Bibliographic record
Abstract
BACKGROUND: A major challenge in the histopathological examination of scalp biopsies is to perform an adequate evaluation of all the hair follicles present in the tissue. Transverse sectioning is currently the preferred technique to demonstrate every follicular structure in a punch biopsy specimen, although diagnostic accuracy is dependent on subjective evaluation of follicular morphology and hair size. OBJECTIVES: To determine if computer-based morphometry and three-dimensional (3D) image reconstruction software can be used to evaluate scalp biopsies from patients with non-cicatricial alopecias. METHODS: Nine 4-mm scalp punches were taken from nine patients with noncicatricial alopecias and step-sectioned transversely at 0.1-mm intervals from the epidermal surface to the subcutaneous fat. Each tissue section was then digitized and analysed using morphometric and 3D image reconstruction software. Morphometric data and 3D images were collated with clinical and conventional light microscopic diagnoses, as well as follow-up information. RESULTS: In four of the nine patients, results of morphometric analysis concurred with conventional clinicopathological diagnoses. In the remaining five patients, morphometry revealed a lower telogen count in one patient and higher telogen count in four patients. One of the four patients with a higher telogen count also had a low mean hair diameter and miniaturized anagen follicles in the 3D image that were suggestive of early androgenetic alopecia (AGA). 3D virtual microscopic imagery allowed the direct visualization of colour-coded, scaled hair follicles which demonstrated characteristic changes in alopecia areata, AGA and telogen effluvium. CONCLUSIONS: Our study demonstrated the feasibility of using morphometric and 3D reconstruction software to evaluate scalp biopsies. With further validation, this technique may prove to be more sensitive to detect subtle quantitative and qualitative follicular changes in non-cicatricial alopecias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".