ArticleEye (London, England)2024
Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.
Article in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 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
- Clinical application of automated optical coherence tomography angiography for retinal fluid segmentation: a study of real-world data.Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning Model for Automated Classification of Macular Neovascularization Subtypes in AMD.Investigative ophthalmology & visual science · 2025Article
- Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance.Bioengineering (Basel, Switzerland) · 2025Review
- Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.Eye (London, England) · 2025Article
- Topographical Quantification of Hyper-Reflective Foci May Predict the Development of Macular Atrophy in Patients With Neovascular Age-Related Macular Degeneration.Investigative ophthalmology & visual science · 2024Article
- Advances in Imaging-Based Machine Learning and Therapeutic Technology in the Management of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Article
- Deep Learning in Neovascular Age-Related Macular Degeneration.Medicina (Kaunas, Lithuania) · 2024Review
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).
methodsIn this IRB-approved study, optical coherence tomography (OCT) data from 50 patients (50 eyes) with exudative nAMD were retrospectively analysed. Two models, A1 and A2, were created based on gradings from two masked readers, R1 and R2. Area under the curve (AUC) values gauged detection performance, and quantification between readers and models was evaluated using Dice and correlation (R
resultsThe deep learning-based algorithms had high accuracies for all fluid types between all models and readers: per B-scan IRF AUCs were 0.953, 0.932, 0.990, 0.942 for comparisons A1-R1, A1-R2, A2-R1 and A2-R2, respectively; SRF AUCs were 0.984, 0.974, 0.987, 0.979; and nPED AUCs were 0.963, 0.969, 0.961 and 0.966. Similarly, the R
conclusionsOur deep learning-based method applied on nAMD can segment critical OCT features with performance akin to manual grading.
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