Abstract
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, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.
Highlights
Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients
CSCs are usually classified into acute or chronic CSC according to the chronicity of the disease, and it is important to evaluate the chronicity of the disease to determine the treatment plan or p rognosis[4]
The 215 patients with CSC were enrolled at the outpatient clinic, with 109 and 106 being diagnosed with acute and chronic CSC, respectively
Summary
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. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. CSC has traditionally been diagnosed using multimodal imaging modalities, including fluorescein angiography (FA) and indocyanine green angiography (ICGA)[4,5] Among these modalities, optical coherence tomography (OCT) is non-invasive, fast, and shows highly reproducible r esults[6,7] and is considered a gold-standard imaging modality for the follow-up of CSC p atients[5]. The prior work reported that the proposed deep learning model can distinguish between 1) normal and CSC and 2) acute and chronic CSC types for a given OCT image[8]. 3D-CNN CNN-LSTM VGG19 + XGB VGG19 + SVM VGG19 + Logistic Regression ResNet-50 + Logistic Regression
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