ArticleScientific reports2021
Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration.
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 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Assessing Polypoidal Choroidal Vasculopathy-Related OCT Features in the TENAYA and LUCERNE Trials.Ophthalmology science · 2026Article
- Long-Term Management of Polypoidal Choroidal Vasculopathy: A Narrative Review.Ophthalmology and therapy · 2026Review
- Imaging techniques for the detection of choroidal neovascularization.Experimental eye research · 2025Review
- Localization of Polypoidal Choroidal Vasculopathy in Fluorescein Angiography Using Semisupervised Deep Learning With Labeled and Unlabeled Images.Translational vision science & technology · 2025Article
- The application of artificial intelligence-generated content in ophthalmology education.Frontiers in medicine · 2025Review
- Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review.International journal of retina and vitreous · 2024Review
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers.Ophthalmology science · 2023Article
- New Concepts for the Diagnosis of Polypoidal Choroidal Vasculopathy.Diagnostics (Basel, Switzerland) · 2023Review
- Association of HERPUD1 genetic variant rs2217332 with age-related macular degeneration and polypoidal choroidal vasculopathy in an Indian cohort.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network.Journal of clinical medicine · 2023Article
- Advances in multi-modal non-invasive imaging techniques in the diagnosis and treatment of polypoidal choroidal vasculopathy.Frontiers in medicine · 2023Review
- Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review.Multimedia tools and applications · 2023Article
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography.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
8 authors.
Funding
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Abstract
Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.
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