Multi-scale and -contrast sensorless adaptive optics optical coherence tomography
Bibliographic record
Abstract
Background: The roles of the retinal microvasculature and the retinal pigment epithelium (RPE) in maintaining the health and metabolic activity of the retina lend great clinical value to their high-resolution visualization. Methods: By integrating polarization diversity detection (PDD) into multi-scale and -contrast sensorless adaptive optics optical coherence tomography (MSC-SAO-OCT), we have developed a novel multi-contrast SAO OCT system for imaging pigment in the RPE as well as flow in the retinal capillaries using OCT angiography (OCTA). Aberration correction was performed based on the image quality using transmissive deformable optical elements. Results: MSC-SAO-OCTA imaging was performed at multiple fields-of-view (FOVs) with adjustable numerical aperture (NA). Retinal flow and RPE structural images for in vivo healthy and pathological human posterior eyes were demonstrated to show clinical feasibility of the system. Conclusions: High-resolution imaging of retinal vasculature at both large and small FOVs, as well as characterization of RPE topology and deformation, enables more sophisticated and concise investigation of retinal pathologies for in vivo human imaging. MSC imaging may permit detection and analysis of even subtle deformations in the RPE layer using a single instrument.
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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 | 0.001 |
| 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 | 0.001 |
| 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".