Cornea characterization using a combined multiphoton microscopy and optical coherence tomography system
Bibliographic record
Abstract
We present a multimodal imaging system which combines multiphoton microscopy and optical coherence tomography to visualize the morphological structures, and to quantify the refractive index (RI) and thickness of cornea. The morphological similarities and differences at different corneal layers across various species are identified. In the piscine and human corneas, the stromata exhibit thin fibers that indicate an overall collagen direction. Human corneas display collagen micro-folds which cause increased light attenuation. In the murine, porcine and bovine corneas, the stromata show interwoven collagen patterns. The Bowman's layer and the Descemet's membrane are also distinguished in some species. The RI and thicknesses are quantified for the epithelium and the stromal layers respectively, where the epithelium is found to have slightly higher RI than the stroma. The average epithelial and stromal RI are, respectively, 1.371 ± 0.016 and 1.360 ± 0.008 for the murine corneas; 1.502 ± 0.057 and 1.335 ± 0.011 for the piscine corneas; 1.433 ± 0.023 and 1.357 ± 0.013 for the human corneas; 1.476 ± 0.091 and 1.343 ± 0.013 for the porcine corneas; and 1.400 ± 0.007 and 1.376 ± 0.003 for the bovine corneas. The multimodal system can potentially provide a comprehensive characterization of the cornea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".