Optical design considerations when imaging the fundus with an adaptive optics correction
Bibliographic record
Abstract
Adaptive Optics (AO) technology has been used in confocal scanning laser ophthalmoscopes (CSLO) which are analogous to confocal scanning laser microscopes (CSLM) with advantages of real-time imaging, increased image contrast, a resistance to image degradation by scattered light, and improved optical sectioning. With AO, the instrumenteye system can have low enough aberrations for the optical quality to be limited primarily by diffraction. Diffraction-limited, high resolution imaging would be beneficial in the understanding and early detection of eye diseases such as diabetic retinopathy. However, to maintain diffraction-limited imaging, sufficient pixel sampling over the field of view is required, resulting in the need for increased data acquisition rates for larger fields. Imaging over smaller fields may be a disadvantage with clinical subjects because of fixation instability and the need to examine larger areas of the retina. Reduction in field size also reduces the amount of light sampled per pixel, increasing photon noise. For these reasons, we considered an instrument design with a larger field of view. When choosing scanners to be used in an AOCSLO, the ideal frame rate should be above the flicker fusion rate for the human observer and would also allow user control of targets projected onto the retina. In our AOCSLO design, we have studied the tradeoffs between field size, frame rate and factors affecting resolution. We will outline optical approaches to overcome some of these tradeoffs and still allow detection of the earliest changes in the fundus in diabetic retinopathy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".