ArticleFrontiers in bioengineering and biotechnology2021
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.BMC ophthalmology · 2025Pooled it
- Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review.International journal of retina and vitreous · 2026Review
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 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
- A Systematic Review of Advances in AI-Assisted Analysis of Fundus Fluorescein Angiography (FFA) Images: From Detection to Report Generation.Ophthalmology and therapy · 2025Review
- 3D Features Fusion for Automated Segmentation of Fluid Regions in CSCR Patients: An OCT-based Photodynamic Therapy Response Analysis.Journal of imaging informatics in medicine · 2025Article
- Predicting glaucoma progression using deep learning framework guided by generative algorithm.Scientific reports · 2023Article
- Intelligent oncology: The convergence of artificial intelligence and oncology.Journal of the National Cancer Center · 2023Review
- Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.Frontiers in medicine · 2023Article
- Retinal Microvascular Signs in Pre- and Early-Stage Diabetic Retinopathy Detected Using Wide-Field Swept-Source Optical Coherence Tomographic Angiography.Journal of clinical medicine · 2022Article
- Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning.Scientific reports · 2022Article
- Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning.Journal of diabetes research · 2022Article
- Wide-field OCT-angiography assessment of choroidal thickness and choriocapillaris in eyes with central serous chorioretinopathy.Frontiers in physiology · 2022Article
- Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program.Frontiers in cell and developmental biology · 2022Article
- Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
23 authors at 8 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074-0.098 logMAR (within four to five letters), and the root mean square errors were 0.096-0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.
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