ArticleInternational journal of ophthalmology2024
Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning.
Article in International journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Correlation between atherogenic index of plasma and retinal vessels in the fundus: a cross-sectional study.European journal of medical research · 2025Article
- Retinal Vessel Geometry and Retinal Abnormalities in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy.Translational vision science & technology · 2025Article
- Deep learning assisted retinal microvasculature assessment and cerebral small vessel disease in Fabry disease.Orphanet journal of rare diseases · 2025Article
- A risk prediction model for neovascular glaucoma secondary to proliferative diabetic retinopathy based on Boruta feature selection and random forest.Frontiers in cell and developmental biology · 2025Article
- Comparative analysis of retinal vascular structural parameters in populations with different glucose metabolism status based on color fundus photography and artificial intelligence.Frontiers in cell and developmental biology · 2025Article
- A semantic segmentation method to analyze retinal vascular parameters of diabetic nephropathy.Frontiers in medicine · 2024Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo develop a deep learning-based model for automatic retinal vascular segmentation, analyzing and comparing parameters under diverse glucose metabolic status (normal, prediabetes, diabetes) and to assess the potential of artificial intelligence (AI) in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes.
methodsRetinal fundus photos from 200 normal individuals, 200 prediabetic patients, and 200 diabetic patients (600 eyes in total) were used. The U-Net network served as the foundational architecture for retinal artery-vein segmentation. An automatic segmentation and evaluation system for retinal vascular parameters was trained, encompassing 26 parameters.
resultsSignificant differences were found in retinal vascular parameters across normal, prediabetes, and diabetes groups, including artery diameter (
conclusionThe deep learning-based model facilitates retinal vascular parameter identification and quantification, revealing significant differences. These parameters exhibit potential as biomarkers for prediabetes and diabetes.
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Registered trials
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