ArticleTranslational vision science & technology2023
Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Drusen volume and reticular pseudodrusen volume from optical coherence tomography with deep learning as risk factors for progression to late age-related macular degeneration in eyes with reticular pseudodrusen and contralateral macular neovascularisation.The British journal of ophthalmology · 2026Article
- Volumetric and En Face Hyper-Reflective Henle Fiber Layer in Age-Related Macular Degeneration.Investigative ophthalmology & visual science · 2026Article
- Automated measurement of macular neovascularization lesion size in nAMD using AI segmentation.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Widefield OCT angiography.Progress in retinal and eye research · 2025Review
- Artificial intelligence-assisted projection-resolved optical coherence tomographic angiography (aiPR-OCTA).Optics express · 2025Article
- Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.Ophthalmology. Retina · 2025Article
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Diffusion model based OCT to OCTA translation.Frontiers in medicine · 2025Article
- Overview of the Use of Optical Coherence Tomography Angiography in Neovascular Age-Related Macular Degeneration.Journal of clinical medicine · 2024Review
- Retinal microvascular changes in patients with pancreatitis and their clinical significance.Scientific reports · 2024Article
- Quantitative Characterization of Retinal Features in Translated OCTA.medRxiv : the preprint server for health sciences · 2024Article
- Quantitative characterization of retinal features in translated OCTA.Experimental biology and medicine (Maywood, N.J.) · 2024Article
- A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images.Diagnostics (Basel, Switzerland) · 2023Review
- OCT angiography and its retinal biomarkers [Invited].Biomedical optics express · 2023Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
Abstract
Purpose: To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning. Methods: A total of 105,66 OCTA scans from 3135 eyes, including 4701 with CNV and 5865 without, were collected in five eye clinics. Both 3 × 3-mm and 6 × 6-mm scans of the central and temporal macula were included. Scans with CNV were collected from multiple diseases, and scans without CNV were collected from both healthy controls and those with multiple diseases. No scans were removed during training or testing due to poor quality. The trained hybrid multitask convolutional neural network outputs a CNV diagnosis and membrane segmentation, respectively. Results: The model demonstrated a highly accurate CNV diagnosis (area under receiver operating characteristic curve = 0.97), achieving a sensitivity of 95% at 95% specificity. The model also correctly segmented CNV lesions (F1 score = 0.78 ± 0.19). Additionally, model performance was comparable on both high-definition 3 × 3-mm scans and low-definition 6 × 6-mm scans. The model did not suffer large performance variations under different diseases. We also show that a subclinical lesion in a patient with neovascular age-related macular degeneration can be monitored over a multiyear time frame using our approach. Conclusions: The proposed method can accurately diagnose and segment CNV in a large real-world clinical data set. Translational Relevance: The algorithm could enable automated CNV screening and quantification in the clinic, which will help improve CNV diagnosis and treatment evaluation.
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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.