Corneal imaging and refractive index measurement using a combined multiphoton microscopy and optical coherence tomography system
Bibliographic record
Abstract
Refractive index (RI) is the optical property of a medium that describes its ability to bend incident light. The corneal refractive index is an especially important measurement in corneal and intraocular refractive surgery where its precise estimation is necessary to obtain accurate surgical outcomes. In this study, we calculated the corneal RI using a combined multiphoton microscopy (MPM) and optical coherence tomography (OCT) system. MPM excites and detects nonlinear signals including two photon excitation fluorescence (TPEF) and second harmonic generation (SHG). TPEF signals are observed from NADH in the cytoplasm, allowing MPM to image the cellular structures in the corneal epithelium and endothelium. SHG signals are observed from collagen, an abundant connective tissue found in the stroma. Optical coherence tomography (OCT) produces cross-sectional, structural images based on the interference fringes created by the reflected light from the sample and reference arms. Our system uses a single sub-10 fs Ti: sapphire laser source which is good for both MPM excitation and OCT resolution. The MPM and OCT images are coregistered when they are taken successively because their axial resolutions are similar and the system shares the laser source and the scanning unit. We can calculate the RI by measuring the optical thickness and the optical path length of the cornea from the MPM and OCT images respectively. We have imaged and calculated the RI of murine and piscine corneas. We were able to see the epithelial, stromal, and endothelial layers and compare their relative thicknesses and the organization of the stromal collagen lamellae. Our results showed that our system can provide both functional and structural information about the cornea and measure the RI of multi-layered tissues.
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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 | 0.001 | 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".