ArticleCommunications medicine2023
An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.Ophthalmology science · 2026Article
- OCTA-ReVABiomedical optics express · 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
- Colour fusion effect on deep learning classification of uveal melanoma.Eye (London, England) · 2024Article
- Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.Investigative ophthalmology & visual science · 2024Article
- Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.Journal of biomedical optics · 2024Article
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
- Color Fusion Effect on Deep Learning Classification of Uveal Melanoma.Research square · 2023Article
- Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.Biomedical optics express · 2023Article
- Deep learning for artery-vein classification in optical coherence tomography angiography.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Mean Arteriolar Diameter Measured from Wide-Field Swept-Source OCT Angiography: A Highly Sensitive Indicator for Mean Arterial Pressure.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDifferential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity.
methodA deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR.
resultsIt was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups.
conclusionsAVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.
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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 Socratic graph.