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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".