Smoothness assessment of corneal stromal surfaces
Bibliographic record
Abstract
PURPOSE: To assess the accuracy of the scanning electron microscopy (SEM) and present alternative approaches to quantify surface roughness based on numerical analysis. SETTING: Department of Ophthalmology, Maisonneuve-Rosemont Hospital, University of Montreal, Montreal, Quebec, Canada. DESIGN: Experimental study. METHODS: Lamellar stromal cuts were performed on human corneas using a femtosecond laser or a microkeratome. The photodisrupted stromal surfaces were processed for SEM, and images were acquired at ×1000 magnification. First, images were evaluated by independent observers. Second, images were analyzed based on first-order and second-order statistics of gray-level intensities. Third, 3-dimensional (3-D) surface reconstructions were generated from pairs of SEM images acquired at 2 angles. RESULTS: Results show that traditional assessment of roughness based on evaluating SEM images by independent observers can be replaced by computer-image texture analysis; an algorithm was developed to avoid subjective and time-consuming observations. The 3-D reconstructions allowed additional characterization of surface properties that was not possible with SEM images alone. Significant fluctuations in surface height were lost, although they could be retrieved using 3-D reconstructions. CONCLUSIONS: Image texture analysis allowed objective and repeatable assessment of stromal surface roughness; however, full assessments of surface-height fluctuations required 3-D reconstruction. These complementary methodologies offer a more comprehensive assessment of corneal surface roughness in clinical applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".