ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2025
Prediction of short-term anatomic prognosis for central serous chorioretinopathy using a generative adversarial network.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Radiomics-based fundus autofluorescence analysis in central serous chorioretinopathy-MICRoN report number twelve.Scientific reports · 2026Article
- Long-term clinical outcomes and prognostic factors of central serous chorioretinopathy.BMC ophthalmology · 2026Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeTo train generative adversarial network (GAN) models to generate predictive optical coherence tomography (OCT) images of central serous chorioretinopathy (CSC) at 3 months after observation using multi-modal OCT images.
methodsFour hundred forty CSC eyes of 440 patients who underwent Cirrus OCT imaging were included. Baseline OCT B-scan images through the foveal center, en face choroid, and en face ellipsoid zone were collected from each patient. The datasets were divided into training and validation (n = 390) and test (n = 50) sets. The input images for each model comprised either baseline B-scan alone or a combination of en face choroid and ellipsoid zones. Predictive post-treatment OCT B-scan images were generated using GAN models and compared with real 3-month images.
resultsOf 50 generated OCT images, there were 48, 47, and 48 acceptable images for UNIT, CycleGAN, and RegGAN, respectively. In comparison with real 3-month images, the generated images showed sensitivity, specificity, and positive predictive values (PPV) for residual fluid in the ranges of 0.762-1.000, 0.483-0.724, and 0.583-0.704; for pigment epithelial detachment (PED) of 0.917-1.000, 0.974-1.000, and 0.917-1.000; and for subretinal hyperreflective material (SHRM) of 0.667-0.778, 0.925-0.950 and 0.700-0.750, respectively. RegGAN exhibited the highest values except for sensitivity.
conclusionsGAN models could generate prognostic OCT images with good performance for prediction of residual fluid, PED, and SHRM presence in CSC. Implementation of the models may help predict disease activity in CSC, facilitating the establishment of a proper treatment plan.
Indexed as
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40032768What OpenQuestion holds
Registered trials
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.