In Vivo Imaging and Morphometry of the Human Pre-Descemet's Layer and Endothelium With Ultrahigh-Resolution Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To visualize in vivo and quantify the thickness of the posterior corneal layers: the acellular pre-Descemet's layer (PDL), Descemet's membrane (DM), and endothelium (END) in healthy subjects, using ultrahigh-resolution optical coherence tomography (UHR-OCT). METHODS: A research-grade, 800-nm UHR-OCT system with 0.95-μm axial resolution in corneal tissue was used to image in vivo the posterior cornea in healthy subjects. The system offers approximately 98 dB sensitivity for 680 μW optical power incident on the cornea and 34,000 A-scans/s image acquisition rate. This study comprised 20 healthy subjects, aged 20 to 60 years. The thickness of the PDL, DM, and END layers was measured both with a custom, automatic segmentation algorithm and manually. RESULTS: The boundaries and structure of the posterior corneal layers were clearly visible in the UHR-OCT images. The average thickness was measured to be 6.6 ± 1.4 μm (PDL), 10.4 ± 2.9 μm (DM), and 4.8 ± 0.4 μm (END), which agrees well with published data from ex vivo studies. Both the END and DM thickness showed minor spatial variations, whereas the PDL showed up to 2× thickness change for different locations on the same cross-sectional corneal image or over the entire imaged region of the cornea. CONCLUSIONS: Our data indicate that all three layers of the posterior cornea can be clearly visualized in vivo and their thicknesses measured precisely with UHR-OCT. Although the PDL thickness showed large spatial variations, the thickness of the DM and END layers was consistent over the entire imaged region of the cornea.
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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 | 0.000 | 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) | 0.001 | 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".