ArticleRetina (Philadelphia, Pa.)2020
QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.
Article in Retina (Philadelphia, Pa.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
- Prediction of visual acuity by macular distortion measured with optical coherence tomography angiography in epiretinal membranes.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Retinal and choroidal changes in diabetic patients before definitive diagnosis of diabetic retinopathy: a two-year prospective study.Journal of endocrinological investigation · 2026Article
- Biomarkers of Preclinical Diabetic Retinopathy Detected by OCT Angiography-A Descriptive Review.Life (Basel, Switzerland) · 2026Review
- Label-free assessment of a microfluidic vessel-on-chip model with visible-light optical tomography reveals structural changes in vascular networks.Lab on a chip · 2026Article
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography.Ophthalmic research · 2026Article
- Association of Serum Vitamin D With Macular Microvascular Structure in Type 2 Diabetic Mellitus Without Diabetic Retinopathy: A Cross-Sectional Study.Journal of diabetes research · 2026Observational
- 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
- GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.Scientific reports · 2025Article
- Article
- Diffusion model based OCT to OCTA translation.Frontiers in medicine · 2025Article
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Macular vessel density in patients with refractory diabetic macular edema in different stages of nonproliferative diabetic retinopathy.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 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
- OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography.Biomedical optics express · 2024Article
- OCTA: Essential or Gimmick?Ophthalmology and therapy · 2024Article
- The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.Sensors (Basel, Switzerland) · 2024Article
- Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.Investigative ophthalmology & visual science · 2024Article
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
14 more citing papers are in PubMed but not listed here.
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6 authors.
Funding
Abstract
purposeThis study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging.
methodsOne hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm.
resultsAmong 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy.
conclusionBlood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.
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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.