  To investigate whether and to what extent central serous chorioretinopathy
(CSC) depicted on color fundus photographs can be assessed using deep learning
technology. We collected a total of 2,504 fundus images acquired on different
subjects. We verified the CSC status of these images using their corresponding
optical coherence tomography (OCT) images. A total of 1,329 images depicted
CSC. These images were preprocessed and normalized. This resulting dataset was
randomly split into three parts in the ratio of 8:1:1 respectively for
training, validation, and testing purposes. We used the deep learning
architecture termed InceptionV3 to train the classifier. We performed
nonparametric receiver operating characteristic (ROC) analyses to assess the
capability of the developed algorithm to identify CSC. The Kappa coefficient
between the two raters was 0.48 (p < 0.001), while the Kappa coefficients
between the computer and the two raters were 0.59 (p < 0.001) and 0.33 (p <
0.05).Our experiments showed that the computer algorithm based on deep learning
can assess CSC depicted on color fundus photographs in a relatively reliable
and consistent way.
