Evaluation of OCT for quantitative in-vivo measurements of changes in neural tissue scattering in longitudinal studies of retinal degeneration in mice
Bibliographic record
Abstract
Optical coherence tomography is an imaging modality that is broadly used in ophthalmic diagnostics. The current generation of OCT systems enables reliable acquisition of volumetric scans containing information about the thicknesses of the various retinal layers. Thus, monitoring layer thickness changes is the main quantitative analysis performed by commercial instruments. In principle, measurements of the OCT signal intensity could also provide information on the health status of the retinal tissue. Unfortunately quantitative measurements and interpretation of scattering changes in retinal OCT is very limited due to variation in overall brightness of the OCT B-scans between imaging sessions. These changes might be caused by variation in alignment or focusing, as well as variation in the quality of the eye’s optics (changes in the tear film, dilation of pupil etc.). Therefore, quantitative analysis of layer intensity requires careful normalization to minimize the effects of such variables. In this manuscript we demonstrate that changes in OCT signal intensity occur in a mouse model of light-induced photoreceptor degeneration. Normalization and quantification of light scattering changes in human patients could likewise lead to improved understanding of clinical OCT data.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".