1. Articles from Jinyoung Han

    1-5 of 5

    1. Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images

      Explore Nature Publishing Group Ophthalmology
      Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images

      Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749 SD-OCT images from 347 patients and 50 healthy controls. To adopt an accurate and robust image classification architecture, we evaluated three well-known CNN structures (VGG-16, VGG-19, and ResNet) and two ...

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      Mentions: Sungkyunkwan University

    2. Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images

      Explore Nature Publishing Group Ophthalmology
      Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images

      Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example ...

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      Mentions: Heidelberg Engineering

    3. Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study

      Explore Nature Publishing Group Ophthalmology
      Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study

      Central serous chorioretinopathy (CSC) is the fourth most common retinopathy and can reduce quality of life. CSC is assessed using optical coherence tomography (OCT), but deep learning systems have not been used to classify CSC subtypes. This study aimed to build a deep learning system model to distinguish CSC subtypes using a convolutional neural network (CNN). We enrolled 435 patients with CSC from a single tertiary center between January 2015 and January 2020. Data from spectral domain OCT (SD-OCT) images of the patients were analyzed using a deep CNN. Five-fold cross-validation was employed to evaluate the model's ability to ...

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      Mentions: Sungkyunkwan University

    4. Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography

      Explore Nature Publishing Group Ophthalmology
      Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography

      This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model's ability to distinguish RAP from PCV. The performances of the ...

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      Mentions: Seoul National University Sungkyunkwan University

    5. Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy

      Explore Nature Publishing Group Ophthalmology
      Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy

      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 ...

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      Mentions: Seoul National University
    1-5 of 5
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    Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images Optical coherence tomography findings in patients with transfusion-dependent β-thalassemia Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea Optical coherence tomography image based eye disease detection using deep convolutional neural network Optical Coherence Tomography Biomarkers in Predicting Treatment Outcomes of Diabetic Macular Edema After Dexamethasone Implants Macular and Optic Disc Parameters in Children with Amblyopic and Nonamblyopic Eyes under Optical Coherence Tomography Fundus Images Optical coherence tomography assessment of the enamel surface after debonding the ceramic brackets using three different techniques Schizophrenia in Translation: Why the Eye?