ArticleScientific reports2021
Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global research trends and hotspots in the pathophysiology, imaging and therapy of type 3 macular neovascularization (MNV3) in age-related macular degeneration (AMD).Frontiers in medicine · 2026Pooled 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
- Anti-VEGF treatment outcome prediction based on optical coherence tomography images in neovascular age-related macular degeneration using a deep neural network.Scientific reports · 2024Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.Journal of medical Internet research · 2023Article
- Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review.Ophthalmology and therapy · 2023Review
- Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.Sensors (Basel, Switzerland) · 2023Article
- Short-term effect of intravitreal brolucizumab injections in patients with neovascular age-related macular degeneration on retinal nerve fiber layer thickness.Scientific reports · 2023Article
- Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network.Journal of clinical medicine · 2023Article
- A retrospective analysis on comparison of optical coherence tomography manifestations among AMD, CEC, PM and ICN and its relationship with vision.Annals of translational medicine · 2022Article
- The effect of intravitreal brolucizumab on choroidal thickness in patients with neovascular age-related macular degeneration.Scientific reports · 2022Article
- An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.Translational vision science & technology · 2022Article
- Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images.Scientific reports · 2022Article
- Diagnosis of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model's ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727-0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.
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