ArticleScientific reports2023
Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- Application of Deep Learning for Advanced Detection and Quantification of Drusen in Nonexudative AMD From Retinal Multispectral Imaging.Translational vision science & technology · 2025Article
- Imaging biomarkers and artificial intelligence for diagnosis, prediction, and therapy of macular fibrosis in age-related macular degeneration: Narrative review and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence in assessing progression of age-related macular degeneration.Eye (London, England) · 2025Review
- Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.Cell reports. Medicine · 2025Article
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Benchmarking diffusion models against state-of-the-art architectures for OCT fluid biomarker segmentation.PloS one · 2025Article
- Role of Artificial Intelligence in Retinal Diseases.Klinische Monatsblatter fur Augenheilkunde · 2024Review
- AI-based support for optical coherence tomography in age-related macular degeneration.International journal of retina and vitreous · 2024Review
- RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.Translational vision science & technology · 2024Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
- Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.Survey of ophthalmologyReview
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.
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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.