ArticleScientific reports2023
Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network.
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 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Prediction of Long-Term Treatment Outcomes for Diabetic Macular Edema Using a Generative Adversarial Network.Translational vision science & technology · 2024Trial
- Longitudinal Forecasting of Retinal Structure and Function Using a Multimodal StyleGAN-Based Architecture.Bioengineering (Basel, Switzerland) · 2026Article
- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 2026Article
- The AI-Augmented Ophthalmologist for chronic ocular diseases: a patient-centered framework for human-AI collaboration.Frontiers in cell and developmental biology · 2026Review
- Predicting visual acuity using optical coherence tomography in patients with neovascular age-related macular degeneration.Scientific reports · 2025Article
- AI-Based Response Classification After Anti-VEGF Loading in Neovascular Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Age-Related Macular Degeneration: Cellular and Molecular Signaling Mechanisms.International journal of molecular sciences · 2025Review
- Prediction of short-term anatomic prognosis for central serous chorioretinopathy using a generative adversarial network.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review.Cureus · 2025Review
- To Treat or Not to Treat? Resolving the Question of Subretinal and Intraretinal Fluid in Age-Related Macular Degeneration: A Narrative Review.Ophthalmology and therapy · 2025Review
- Evolutionary patterns and research frontiers of artificial intelligence in age-related macular degeneration: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2025Article
- ANGPTL4 promotes choroidal neovascularization and subretinal fibrosis through the endothelial‒mesenchymal transition.International ophthalmology · 2024Article
- Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Enhanced Point-of-Care SARS-CoV-2 Detection: Integrating RT-LAMP with Microscanning.Biosensors · 2024Article
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Deep Learning in Neovascular Age-Related Macular Degeneration.Medicina (Kaunas, Lithuania) · 2024Review
- Artificial intelligence in age-related macular degeneration: state of the art and recent updates.BMC ophthalmology · 2024Review
- Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
To develop an artificial intelligence (AI) model that predicts anti-vascular endothelial growth factor (VEGF) agent-specific anatomical treatment outcomes in neovascular age-related macular degeneration (AMD), thereby assisting clinicians in selecting the most suitable anti-VEGF agent for each patient. This retrospective study included patients diagnosed with neovascular AMD who received three loading injections of either ranibizumab or aflibercept. Training was performed using optical coherence tomography (OCT) images with an attention generative adversarial network (GAN) model. To test the performance of the AI model, the sensitivity and specificity to predict the presence of retinal fluid after treatment were calculated for the AI model, an experienced (Examiner 1), and a less experienced (Examiner 2) human examiners. A total of 1684 OCT images from 842 patients (419 treated with ranibizumab and 423 treated with aflibercept) were used as the training set. Testing was performed using images from 98 patients. In patients treated with ranibizumab, the sensitivity and specificity, respectively, were 0.615 and 0.667 for the AI model, 0.385 and 0.861 for Examiner 1, and 0.231 and 0.806 for Examiner 2. In patients treated with aflibercept, the sensitivity and specificity, respectively, were 0.857 and 0.881 for the AI model, 0.429 and 0.976 for Examiner 1, and 0.429 and 0.857 for Examiner 2. In 18.5% of cases, the fluid status of synthetic posttreatment images differed between ranibizumab and aflibercept. The AI model using GAN might predict anti-VEGF agent-specific short-term treatment outcomes with relatively higher sensitivity than human examiners. Additionally, there was a difference in the efficacy in fluid resolution between the anti-VEGF agents. These results suggest the potential of AI in personalized medicine for patients with neovascular AMD.
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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.