ArticleInternational journal of ophthalmology2024
Retinal vascular morphological characteristics in diabetic retinopathy: an artificial intelligence study using a transfer learning system to analyze ultra-wide field images.
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.
- Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal integration.Frontiers in medicine · 2026Review
- AI-Based Quantitative Assessment of Retinal Vascular Morphology in Circumscribed Choroidal Hemangioma.Ophthalmology and therapy · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework.Frontiers in cell and developmental biology · 2025Article
- Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years.Healthcare (Basel, Switzerland) · 2024Article
- Exploring the effect of gestational diabetes mellitus on retinal vascular morphology by PKSEA-Net.Frontiers in cell and developmental biology · 2024Article
- A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs.Frontiers in cell and developmental biology · 2024Article
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9 authors.
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Abstract
aimTo investigate the morphological characteristics of retinal vessels in patients with different severity of diabetic retinopathy (DR) and in patients with or without diabetic macular edema (DME).
methodsThe 239 eyes of DR patients and 100 eyes of healthy individuals were recruited for the study. The severity of DR patients was graded as mild, moderate and severe non-proliferative diabetic retinopathy (NPDR) according to the international clinical diabetic retinopathy (ICDR) disease severity scale classification, and retinal vascular morphology was quantitatively analyzed in ultra-wide field images using RU-net and transfer learning methods. The presence of DME was determined by optical coherence tomography (OCT), and differences in vascular morphological characteristics were compared between patients with and without DME.
resultsRetinal vessel segmentation using RU-net and transfer learning system had an accuracy of 99% and a Dice metric of 0.76. Compared with the healthy group, the DR group had smaller vessel angles (33.68±3.01
conclusionIn this study, an artificial intelligence retinal vessel segmentation system is used with 99% accuracy, thus providing with relatively satisfactory performance in the evaluation of quantitative vascular morphology. DR patients have a tendency of vascular occlusion and dropout. The presence of DME does not compromise the integral retinal vascular pattern.
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