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Using Retinal Imaging to Study Dementia
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Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated

Xu Han Shi1,2,3, Li Dong1,2,3, Rui Heng Zhang1,2,3

  • 1Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

Frontiers in Cell and Developmental Biology
|July 7, 2023
PubMed
Summary

Artificial intelligence analysis of retinal vascular characteristics reveals significant correlations with cognitive function. Decreased vascular fractal dimension and density may indicate early cognitive impairment.

Keywords:
artificial intelligencecognitive functioncognitive impairmentdeep learningretinal vascular

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Area of Science:

  • Ophthalmology
  • Neurology
  • Artificial Intelligence

Background:

  • Cognitive impairment affects millions globally, with early detection crucial for intervention.
  • Retinal vasculature may reflect cerebrovascular health, offering a potential window into cognitive status.

Purpose of the Study:

  • To investigate the relationship between retinal vascular morphological parameters and cognitive function.
  • To utilize artificial intelligence for automated quantitative measurements of retinal vasculature.

Main Methods:

  • A deep learning model (ResNet101-UNet) was employed for automated retinal vascular segmentation on fundus photographs.
  • Retinal vascular parameters (branching angle, fractal dimension, diameter, tortuosity, density) were measured in 3107 participants (aged 50-93).
  • Cognitive function was assessed using the Mini-Mental State Examination (MMSE).

Main Results:

  • Mild cognitive impairment was associated with larger venular diameter and smaller vascular fractal dimension and density.
  • Severe cognitive impairment showed reduced arteriole-to-venular ratio and vascular fractal dimension compared to mild impairment.
  • Higher MMSE scores correlated with higher retinal vascular fractal dimension and density in multivariate analysis.

Conclusions:

  • AI-driven analysis of retinal vasculature reveals significant correlations with cognitive impairment.
  • Reduced retinal vascular fractal dimension and density are potential biomarkers for early cognitive impairment detection.
  • The retinal arteriole-to-venular ratio may indicate later stages of cognitive decline.