ArticleTranslational vision science & technology2020
Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent advances and applications of optical coherence tomography angiography in diabetic retinopathy.Frontiers in endocrinology · 2025Pooled it
- Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images.Quantitative imaging in medicine and surgery · 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
- Optical coherence tomography angiography in neovascular age-related macular degeneration: comprehensive review of advancements and future perspective.Eye (London, England) · 2025Review
- Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Article
- Diffusion model based OCT to OCTA translation.Frontiers in medicine · 2025Article
- 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
- 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
- Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence.World journal of methodology · 2024Review
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
- Quantitative Characterization of Retinal Features in Translated OCTA.medRxiv : the preprint server for health sciences · 2024Article
- Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions.Diagnostics (Basel, Switzerland) · 2024Article
- The application of artificial intelligence in diabetic retinopathy: progress and prospects.Frontiers in cell and developmental biology · 2024Review
- Quantitative characterization of retinal features in translated OCTA.Experimental biology and medicine (Maywood, N.J.) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy. Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform. Results: With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment. Conclusions: With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients. Translational Relevance: Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.
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