ArticleFrontiers in bioengineering and biotechnology2021
Deep Learning for Detecting Subretinal Fluid and Discerning Macular Status by Fundus Images in Central Serous Chorioretinopathy.
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 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.BMC ophthalmology · 2025Pooled it
- Radiomics-based fundus autofluorescence analysis in central serous chorioretinopathy-MICRoN report number twelve.Scientific reports · 2026Article
- An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images.International journal of ophthalmology · 2026Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- Can YOLO Detect Retinal Pathologies? A Step Towards Automated OCT Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Simple Code Implementation for Deep Learning-Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography.Translational vision science & technology · 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
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Subretinal fluid (SRF) can lead to irreversible visual loss in patients with central serous chorioretinopathy (CSC) if not absorbed in time. Early detection and intervention of SRF can help improve visual prognosis and reduce irreversible damage to the retina. As fundus image is the most commonly used and easily obtained examination for patients with CSC, the purpose of our research is to investigate whether and to what extent SRF depicted on fundus images can be assessed using deep learning technology. In this study, we developed a cascaded deep learning system based on fundus image for automated SRF detection and macula-on/off serous retinal detachment discerning. The performance of our system is reliable, and its accuracy of SRF detection is higher than that of experienced retinal specialists. In addition, the system can automatically indicate whether the SRF progression involves the macula to provide guidance of urgency for patients. The implementation of our deep learning system could effectively reduce the extent of vision impairment resulting from SRF in patients with CSC by providing timely identification and referral.
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Registered trials
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