ArticleScientific reports2021
A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Recent advances and applications of optical coherence tomography angiography in diabetic retinopathy.Frontiers in endocrinology · 2025Pooled it
- Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Data Set.Ophthalmology science · 2026Article
- MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.Journal of imaging · 2026Article
- Applicability study of AI attribution methods for ophthalmic image classification.Scientific reports · 2026Article
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Cross-modal fusion of OCT and OCT angiography enface for improved diagnostics of diabetic retinopathy.Frontiers in medicine · 2026Article
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Modern Approach to Diabetic Retinopathy Diagnostics.Diagnostics (Basel, Switzerland) · 2024Review
- Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE).Bioengineering (Basel, Switzerland) · 2024Article
- Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy.Biomedicines · 2024Review
- Review
- Comparison of Widefield OCT Angiography Features Between Severe Non-Proliferative and Proliferative Diabetic Retinopathy.Ophthalmology and therapy · 2024Article
- Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy.Data in brief · 2024Article
- CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing human labor by processing large amounts of data in a shorter time. We developed a fully automated classification algorithm to diagnose DR and identify referable status using optical coherence tomography angiography (OCTA) images with convolutional neural network (CNN) model and verified its feasibility by comparing its performance with that of conventional machine learning model. Ground truths for classifications were made based on ultra-widefield fluorescein angiography to increase the accuracy of data annotation. The proposed CNN classifier achieved an accuracy of 91-98%, a sensitivity of 86-97%, a specificity of 94-99%, and an area under the curve of 0.919-0.976. In the external validation, overall similar performances were also achieved. The results were similar regardless of the size and depth of the OCTA images, indicating that DR could be satisfactorily classified even with images comprising narrow area of the macular region and a single image slab of retina. The CNN-based classification using OCTA is expected to create a novel diagnostic workflow for DR detection and referral.
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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.