Macular choriocapillaris perfusion area: a potential biomarker of mild cognitive impairment in patients with cerebral small vessel disease
Bibliographic record
Abstract
Aim To develop and validate retinal vascular biomarkers for detecting mild cognitive impairment (MCI) in cerebral small vessel disease (CSVD) using swept-source optical coherence tomography angiography (SS-OCTA). Methods Participants with MCI and normal cognition were prospectively enrolled from two ongoing cohorts (Dream-10 and FRESH-CSVD; NCT06164262 and NCT06431711 ). All participants underwent SS-OCTA and structural MRI (S-MRI). Individuals with Alzheimer’s disease were excluded based on plasma biomarkers. Participants were split into development (January–August 2024) and temporal validation (September 2024–January 2025) cohorts. Feature selection was conducted using least absolute shrinkage and selection operator regression, followed by receiver operating characteristic analyses. Results A total of 209 participants were included, with 48.8% (102/209) diagnosed with MCI. In the development cohort (n=136), the 3–6 mm macular choriocapillaris perfusion area (CCPA) of the left eye (oculus sinister, OS) showed superior diagnostic accuracy for MCI (AUC=0.906), outperforming S-MRI markers (all p < 0.05). Temporal validation confirmed diagnostic accuracy (AUC 0.902; sensitivity 88.6%, specificity 81.3%) with minimal performance drift (ΔAUC 0.002). Adding S-MRI markers did not significantly enhance diagnostic performance (p>0.05). Both 0–3 and 3–6 mm OS macular CCPA were significantly associated with cognitive decline (Mini-Mental State Examination, Montreal Cognitive Assessment and Clinical Dementia Rating Sum of Boxes; all p < 0.01), and mediation analyses suggested partial effects through white matter hyperintensity volume and right choroid plexus volume ratio. Conclusion SS-OCTA-derived macular CCPA, especially in the 3–6 mm OS region, may serve as a promising and non-invasive biomarker for CSVD-related MCI. Further multicentre studies are needed to establish its clinical applicability.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".