Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning
This addresses the need for automated diagnosis of CSC in ophthalmology, but it is incremental as it applies an existing deep learning method to a specific medical imaging task.
The study tackled the problem of assessing central serous chorioretinopathy (CSC) from color fundus photographs using deep learning, achieving Kappa coefficients of 0.59 and 0.33 between the computer algorithm and human raters.
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.