ArticleThe British journal of ophthalmology2018
Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study.
Article in The British journal of ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04215445 (Effect of Sodium Glucose co Transporter 2), which is not on this map. Cited by 33 papers.
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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.
Effect of Sodium Glucose co Transporter 2 (SGLT2) Inhibition on Optical Coherence Tomography Angiography (OCT-A) Parameters in Diabetic Chronic Kidney Disease (CKD)
Who cites it
33 citing papers in PubMed, 92 citations in OpenAlex.
- Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.Eye (London, England) · 2024Observational
- Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE).Bioengineering (Basel, Switzerland) · 2024Article
- Optical Coherence Tomography Angiography as a Diagnostic Tool for Diabetic Retinopathy.Diagnostics (Basel, Switzerland) · 2024Review
- Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map.IEEE transactions on bio-medical engineering · 2024Article
- Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT.Ophthalmology science · 2023Article
- Is preclinical diabetic retinopathy in diabetic nephropathy individuals more severe?Frontiers in endocrinology · 2023Article
- Deep learning-based signal-independent assessment of macular avascular area on 6×6 mm optical coherence tomography angiogram in diabetic retinopathy: a comparison to instrument-embedded software.The British journal of ophthalmology · 2023Article
- Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images.Sensors (Basel, Switzerland) · 2022Article
- A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.Translational vision science & technology · 2022Article
- A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images.Scientific reports · 2022Article
- A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography.Translational vision science & technology · 2022Article
- Ginsenoside Rg1 Inhibits High Glucose-Induced Proliferation, Migration, and Angiogenesis in Retinal Endothelial Cells by Regulating the lncRNA SNHG7/miR-2116-5p/SIRT3 Axis.Journal of oncology · 2022Article
- A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.Scientific reports · 2021Article
- Optical coherence tomography angiography in the management of diabetic retinopathy.Indian journal of ophthalmology · 2021Review
- Normative intercapillary distance and vessel density data in the temporal retina assessed by wide-field spectral-domain optical coherence tomography angiography.Experimental biology and medicine (Maywood, N.J.) · 2021Article
- Machine learning in optical coherence tomography angiography.Experimental biology and medicine (Maywood, N.J.) · 2021Review
- DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography.IEEE transactions on bio-medical engineering · 2021Article
- Evans blue staining to detect deep blood vessels in peripheral retina for observing retinal pathology in early-stage diabetic rats.International journal of ophthalmology · 2021Article
- Imaging Motion: A Comprehensive Review of Optical Coherence Tomography Angiography.Advances in experimental medicine and biology · 2021Review
- Macular Vessel Density in Diabetic Retinopathy Patients: How Can We Accurately Measure and What Can It Tell Us?Clinical ophthalmology (Auckland, N.Z.) · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOptical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images.
methodsThis was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features-blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)-were segmented from these images and used to train a new, automated classifier.
resultsOne hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%.
conclusionAutomated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.
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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.