ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2018
Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 4 of them syntheses that pooled it.
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Who cites it
96 citing papers in PubMed, 4 syntheses or guidelines pooled it, 244 citations in OpenAlex.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review.Eye (London, England) · 2023Pooled it
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.Current diabetes reports · 2019Pooled it
- A deep learning-based pachychoroid index based on choroidal image patterns of central serous chorioretinopathy using enhanced-depth-imaging optical coherence tomography.Japanese journal of ophthalmology · 2026Observational
- Investigating the Impact of Semi-Supervised Learning Methods to Improve the Quality of Diagnosis of Retinal Diseases from OCT Images.Diagnostics (Basel, Switzerland) · 2026Article
- Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.Scientific data · 2026Article
- Low-Rank Fine-Tuning Meets Cross-modal Analysis: A Robust Framework for Age-Related Macular Degeneration Categorization.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence based prediction of first recurrence in neovascular age related macular degeneration with validation by 19 experts.Scientific reports · 2026Article
- Article
- Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.AJO international · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- Enhancement of Optical Coherence Tomography Images Using Adversarial Neural Networks: Impacts on Ophthalmic Practice.Cureus · 2025Article
- Artificial intelligence in assessing progression of age-related macular degeneration.Eye (London, England) · 2025Review
- From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification.Journal of medical signals and sensors · 2025Article
- AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography.Biomedical optics express · 2024Article
- AMD-SD: An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation.Scientific data · 2024Article
- Grad-CAM-Based Investigation into Acute-Stage Fluorescein Angiography Images to Predict Long-Term Visual Prognosis of Branch Retinal Vein Occlusion.Journal of clinical medicine · 2024Article
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Macular edema in retinal fundus images by a computational algorithmRevista medica del Instituto Mexicano del Seguro Social · 2024Article
36 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeOur purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT).
methodsA total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD.
resultsAfter an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p < 0.001).
conclusionsWith a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.
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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.