ArticleBMC geriatrics2024
Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation.
Article in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- Retinal Vascularity Index in Mild Cognitive Impairment.Translational vision science & technology · 2026Article
- Reinforcement learning-driven adaptive game therapy for cognitive impairment patients with improved vision transformer based detection model.BMC psychology · 2026Article
- Advancing Brain and Body Vascular Imaging in Bipolar Disorder: A Report From the International Society for Bipolar Disorders Vascular Task Force.Bipolar disorders · 2026Review
- Artificial intelligence and neurodegeneration: state of the art and explainability.International journal of retina and vitreous · 2026Review
- Retinal microvascular dysfunction in mild cognitive impairment: Associations with cerebral small vessel disease, plasma biomarkers, and cognitive decline.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Relationships between multimodal ocular imaging and white matter hyperintensity volume.Quantitative imaging in medicine and surgery · 2025Article
- Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images.Bioengineering (Basel, Switzerland) · 2025Article
- Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.Scientific reports · 2025Article
- Multi-task deep learning for predicting metabolic syndrome from retinal fundus images in a Japanese health checkup dataset.PloS one · 2025Article
- Advances in ocular aging: combining deep learning, imaging, and liquid biopsy biomarkers.Frontiers in medicine · 2025Review
- The Optical Nature of Myopic Changes in Retinal Vessel Caliber.Ophthalmology scienceArticle
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Authors and funding
18 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition.
methodsWe included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P < 0.05 was considered statistically significant. The correlation between the CI and RMPs were explored, then the correlation coefficients between CI and RMPs were analyzed. Random Forest nonlinear classification model was used to predict whether one having cognitive decline or not. The assessment criterion was the AUC value derived from the working characteristic curve.
resultsThe fractal dimension (FD) and global vein width were significantly correlated with the CI (P < 0.05). Age (0.193), BMI (0.154), global vein width (0.106), retinal vessel FD (0.099), and CRAE (0.098) were the variables in this model that were ranked in order of feature importance. The AUC values of the model were 0.799.
conclusionsEstablishment of a predictive model based on the extraction of vascular features from fundus images has a high recognizability and predictive power for cognitive function and can be used as a screening method for CI.
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