Purpose: To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology. Methods: 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 images. A total of 1,329 images depicted CSC. These images were preprocessed and normalized. This resulting data set 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 Inception-V3 to train the classifier. We performed nonparametric receiver operating characteristic analyses to assess the capability of the developed algorithm to identify CSC. To study the inter-reader variability and compare the performance of the computerized scheme and human experts, we asked two ophthalmologists (i.e., Rater #1 and #2) to independently review the same testing data set in a blind manner. We assessed the performance difference between the computer algorithms and the two experts using the receiver operating characteristic curves and computed their pair-wise agreements using Cohen's Kappa coefficients. Results: The areas under the receiver operating characteristic curve for the computer, Rater #1, and Rater #2 were 0.934 (95% confidence interval = 0.905-0.963), 0.859 (95% confidence interval = 0.809-0.908), and 0.725 (95% confidence interval = 0.662-0.788). 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). Conclusion: 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.