ArticleScientific reports2020
Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 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
- Multi-Modal AI for Multi-Label Retinal Disease Prediction Using OCT and Fundus Images: A Hybrid Approach.Sensors (Basel, Switzerland) · 2025Article
- Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.Journal of medical Internet research · 2023Article
- Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)-An Early Imaging Biomarker in Diabetic Retinopathy.Translational vision science & technology · 2023Article
- Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.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
- DeepPDT-Net: predicting the outcome of photodynamic therapy for chronic central serous chorioretinopathy using two-stage multimodal transfer learning.Scientific reports · 2022Article
- Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases.Translational vision science & technology · 2022Article
- Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach.Scientific reports · 2022Article
- Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning.Scientific reports · 2022Article
- Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images.Scientific reports · 2022Article
- Simple Code Implementation for Deep Learning-Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography.Translational vision science & technology · 2022Article
- Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study.Scientific reports · 2022Article
- A review of generative adversarial network applications in optical coherence tomography image analysis.Journal of optometry · 2022Review
- An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.Frontiers in public health · 2022Review
- Artificial intelligence for improving sickle cell retinopathy diagnosis and management.Eye (London, England) · 2021Review
- Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.Scientific reports · 2021Article
- Diagnosis of central serous chorioretinopathy by deep learning analysis of en face images of choroidal vasculature: A pilot study.PloS one · 2021Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Central serous chorioretinopathy (CSC) is a common condition characterized by serous detachment of the neurosensory retina at the posterior pole. We built a deep learning system model to diagnose CSC, and distinguish chronic from acute CSC using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of patients with CSC and a control group were analyzed with a convolutional neural network. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model. For CSC diagnosis, our model showed an accuracy, sensitivity, and specificity of 93.8%, 90.0%, and 99.1%, respectively; AUROC was 98.9% (95% CI, 0.983-0.995); and its diagnostic performance was comparable with VGG-16, Resnet-50, and the diagnoses of five different ophthalmologists. For distinguishing chronic from acute cases, the accuracy, sensitivity, and specificity were 97.6%, 100.0%, and 92.6%, respectively; AUROC was 99.4% (95% CI, 0.985-1.000); performance was better than VGG-16 and Resnet-50, and was as good as the ophthalmologists. Our model performed well when diagnosing CSC and yielded highly accurate results when distinguishing between acute and chronic cases. Thus, automated deep learning system algorithms could play a role independent of human experts in the diagnosis of CSC.
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