ArticleBiomedical optics express2024
AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Radiomics-based fundus autofluorescence analysis in central serous chorioretinopathy-MICRoN report number twelve.Scientific reports · 2026Article
- A Three-dimensional Analytical Framework for Retinal Microvasculature Reveals Layer-associated Vulnerability in Development and Neovascular Remodeling.bioRxiv : the preprint server for biology · 2026Article
- 3D-OCTA Evaluation of Retinal Microcirculation Abnormalities in the Macular Region of Eyes With Anisometropic Amblyopia.Translational vision science & technology · 2025Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation.Scientific data · 2025Article
- Imaging biomarkers in optic neuritis: current tools and future directions.Frontiers in neurology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal vasculature is the only vascular system in the human body that can be observed in a non-invasive manner, with a phenotype associated with a wide range of ocular, cerebral, and cardiovascular diseases. OCT and OCT angiography (OCTA) provide powerful imaging methods to visualize the three-dimensional morphological and functional information of the retina. In this study, based on OCT and OCTA multimodal inputs, a multitask convolutional neural network model was built to realize 3D segmentation of retinal blood vessels and disease classification for different retinal diseases, overcoming the limitations of existing methods that can only perform 2D analysis of OCTA. Two hundred thirty sets of OCT and OCTA data from 109 patients, including 138,000 cross-sectional images in normal and diseased eyes (age-related macular degeneration, retinal vein occlusion, and central serous chorioretinopathy), were collected from four commercial OCT systems for model training, validation, and testing. Experimental results verified that the proposed method was able to achieve a DICE coefficient of 0.956 for 3D segmentation of blood vessels and an accuracy of 91.49% for disease classification, and further enabled us to evaluate the 3D reconstruction of retinal vessels, explore the interlayer connections of superficial and deep vasculatures, and reveal the 3D quantitative vessel characteristics in different retinal diseases.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.