Retinal Vascularity Index in Mild Cognitive Impairment
Purpose: The purpose of this study was to determine whether retinal vascularity index (RVI) differs between individuals with mild cognitive impairment (MCI) and normal cognition, and to assess RVI as a novel ultra-widefield (UWF) implementation of retinal arteriovenous assessment. Methods: In a cross-sectional case-control study, UWF fundus color images from patients with MCI and cognitively healthy controls were analyzed using a semi-automated platform via the Comprehensive Ocular Imaging Network (COIN; ocularimaging.net). RVI was calculated as the ratio of arterial-to-venous cross-sectional area within a 47-degree region of interest (ROI) centered on the optic disc, with quadrant-based segmentation producing regional RVI values for 4 quadrants within the 47-degree ROI. Generalized estimating equations (GEEs) accounted for inter-eye correlation. Results: Fifty-five eyes of 38 patients with MCI and 89 eyes of 55 cognitively healthy patients were included. Mean RVI across the 47-degree ROI was reduced in MCI versus control eyes (0.524 vs. 0.730, P < 0.001) after adjusting for treated hypertension. Reduced RVI was also observed in each of the four quadrants in MCI compared with controls (P < 0.001). RVI showed good discriminative ability (area under the curve [AUC] = 0.86, sensitivity = 80%, and specificity = 79%) with good inter-grader reliability (intraclass correlation coefficient [ICC] = 0.840, 95% confidence interval [CI] = 0.686-0.943). Conclusions: Mean RVI across the 47-degree ROI was significantly lower in individuals with MCI compared with cognitively healthy controls. Translational Relevance: RVI, derived from UWF imaging, may reflect retinal microvascular cross-sectional area differences associated with MCI. As an exploratory metric, further validation is needed before RVI can be used as a noninvasive tool to complement diagnostic approaches for MCI.
Authors
- Sharon Fekrat (ORCID: https://orcid.org/0000-0003-4403-5996)
- Alex Choi (ORCID: https://orcid.org/0000-0003-2220-8024)
- Atharva Hudlikar (ORCID: https://orcid.org/0000-0003-0151-2254)
- Rachel Song
- Kim G. Johnson (ORCID: https://orcid.org/0000-0002-8793-2489)
- Dilraj Singh Grewal
- Alice Haystead
- Michael Zhu
- Rachel D’Cunha (ORCID: https://orcid.org/0009-0004-8474-2841)
- John Hsu (ORCID: https://orcid.org/0009-0002-8318-3828)
- Hans Ng
- Jenny Melcher
- Rupesh Agrawal
- Sandra Stinnett
- Arnav Sharma
Institutions
- Duke University (US)
- Nanyang Technological University (SG)
- Tan Tock Seng Hospital (SG)
- Singapore Eye Research Institute (SG)
- Duke Medical Center (US)
- National Healthcare Group (SG)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2026-09-30
- DOI
- https://doi.org/10.1167/tvst.15.9.23
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00