ArticleBiomedical optics express2020
AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
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Who cites it
28 citing papers in PubMed.
- Cross-modal fusion of OCT and OCT angiography enface for improved diagnostics of diabetic retinopathy.Frontiers in medicine · 2026Article
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- OCTA-ReVABiomedical optics express · 2025Article
- Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images.Scientific reports · 2025Article
- Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.Journal of biomedical optics · 2025Article
- Differential artery-vein analysis in OCTA for predicting the anti-VEGF treatment outcome of diabetic macular edema.Biomedical optics express · 2025Article
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.Investigative ophthalmology & visual science · 2024Article
- OCTA-based AMD Stage Grading Enhancement via Class-Conditioned Style Transfer.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
- Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.Journal of personalized medicine · 2024Review
- Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2023Article
- OCT angiography and its retinal biomarkers [Invited].Biomedical optics express · 2023Review
- Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2023Article
- Cardiac Adipose Tissue Segmentation via Image-Level Annotations.IEEE journal of biomedical and health informatics · 2023Article
- Deep learning for artery-vein classification in optical coherence tomography angiography.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography.Communications medicine · 2023Article
- Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review.Ophthalmology and therapy · 2023Review
- Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.
Identifiers
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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 OpenQuestion graph.