Diagnosing neurodegenerative disorders using retina as an external window: A systematic review of OCT-MRI correlations
Bibliographic record
Abstract
BackgroundRecent studies have explored optical coherence tomography (OCT) and OCT-angiography (OCT-A) as biomarkers for Alzheimer's disease (AD). However, correlations between OCT/OCT-A and neurodegeneration metrics remain underexplored.ObjectiveWe performed a systematic review of OCT/OCT-A and structural brain imaging using MRI across various neurodegenerative disorders.MethodsWe searched Medline, Embase, and various other databases from January to June 2023 using keywords regarding neurodegenerative conditions and OCT/OCT-A. Out of 2962 citations. 93 articles were reviewed, and 28 met our inclusion criteria.ResultsLayer-or-region-specific retinal metrics were the most promising for non-vascular neurodegeneration, while vascular retinal parameters had the unique capacity to reflect vascular lesions. Both types of biomarkers correlated with global brain atrophy. Microstructural brain alterations best correlated with layer-specific thinning of retina.ConclusionsA better understanding of associations between retinal and brain lesions could eventually lead to the clinical application of retinal biomarkers for the early diagnosis of neurodegenerative conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".