ADAPTIVE OPTICS IMAGING IN DIABETIC RETINOPATHY
Bibliographic record
Abstract
PURPOSE: To investigate the correlation between diabetic retinopathy (DR) severity and microscopic retinal and vascular alterations using adaptive optics imaging. METHODS: In this single-center, prospective cohort study, adult participants with healthy eyes or DR underwent adaptive optics imaging. Participants were classified into control/mild nonproliferative DR, moderate/severe nonproliferative DR, and proliferative DR. Adaptive optics imaging using the RTX1 camera was obtained from 48 participants (87 eyes) for photoreceptor data and from 36 participants (62 eyes) for vascular data. RESULTS: Photoreceptor parameters significantly differed between DR groups at 2° and 4° of retinal eccentricity. Wall-to-lumen ratio varied significantly at 2° eccentricity, while other vascular parameters remained nonsignificant. Cone density and dispersion were the strongest predictors for DR severity ( P < 0.001) in multivariable generalized estimating equation modeling, while other vascular parameters remained nonsignificant between DR severity groups. All photoreceptor parameters showed significant correlations with visual acuity overall and across most DR severity groups. CONCLUSION: To date, this is one of the largest studies evaluating the use of adaptive optics imaging in DR. Adaptive optics imaging was demonstrated to differentiate between various levels of disease severity in DR. These results support the potential role in diagnostic and therapeutic microstructural evaluation in research and clinical practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".