ArticleScientific reports2022
Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 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
- Long-term visual outcomes and development of retinal pigment epithelial and outer retinal atrophy in neovascular age-related macular degeneration: a comparison of conventional and novel classifications.BMC ophthalmology · 2026Article
- Advancing AMD Detection: Dataset Design and Deep Learning Optimization for Unconstrained Retinal Images.Vision (Basel, Switzerland) · 2026Article
- Localization of Polypoidal Choroidal Vasculopathy in Fluorescein Angiography Using Semisupervised Deep Learning With Labeled and Unlabeled Images.Translational vision science & technology · 2025Article
- Classifying retinal diseases via pyramid vision graph convolutional network for optical coherence tomography images.Biomedical optics express · 2025Article
- Advancing predictive, preventive, and personalized medicine in eyelid diseases: a concerns-based and expandable screening system through structural dissection.The EPMA journal · 2025Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).Scientific reports · 2023Article
- Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.Sensors (Basel, Switzerland) · 2023Article
- Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network.Journal of clinical medicine · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- A cascade eye diseases screening system with interpretability and expandability in ultra-wide field fundus images: A multicentre diagnostic accuracy study.EClinicalMedicine · 2022Article
- 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
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749 SD-OCT images from 347 patients and 50 healthy controls. To adopt an accurate and robust image classification architecture, we evaluated three well-known CNN structures (VGG-16, VGG-19, and ResNet) and two customized classification layers (fully connected layer with dropout vs. global average pooling). Following the test set performance, the model with the highest classification accuracy was used. Transfer learning and data augmentation were applied to improve the robustness and accuracy of the model. Our proposed model showed an accuracy of 87.4% on the test data (920 images), scoring higher than ten ophthalmologists, for the same data. Additionally, the part that our model judged to be important in classification was confirmed through Grad-CAM images, and consequently, it has a similar judgment criteria to that of ophthalmologists. Thus, we believe that our model can be used as an auxiliary tool in clinical practice.
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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.