Photoreceptor Characteristics in Diabetic Retinopathy vs Controls Using Adaptive Optics Imaging: Systematic Review
Bibliographic record
Abstract
Purpose: To assess the differences in morphological photoreceptor outcomes measured using adaptive optics (AO)–assisted imaging between individuals with diabetes or prediabetes and healthy controls. Methods: A systematic search was conducted across MEDLINE, Embase, and Cochrane databases from January 2000 to June 2023. Studies that used AO-assisted imaging modalities to quantitatively compare photoreceptor outcomes in patients with diabetes or prediabetes with healthy controls were included. Results: Eleven studies consisting of 551 eyes were included. Most studies reported significant differences in photoreceptor outcomes between diabetic and healthy populations, particularly as diabetic retinopathy (DR) severity increased. Cone regularity was the most sensitive parameter for detecting significant differences between groups. AO imaging was less reliable in distinguishing individuals with diabetes without DR or with mild DR severity from controls. Conclusions: AO imaging showed promise in detecting significant differences associated with diabetes and DR, in particular with increasing disease severity. Further research is warranted to assess AO’s utility as a diabetes and DR screening tool. Standardizing imaging protocols in future studies is recommended to allow for more direct quantitative comparisons. These findings highlight the current evidence on photoreceptor changes in patients with diabetes and the potential of AO in advancing diabetic eye care.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".