ASSESSMENT OF CENTRAL SEROUS CHORIORETINOPATHY DEPICTED ON COLOR FUNDUS PHOTOGRAPHS USING DEEP LEARNING

作者：Yi Zhen, Hang Chen, Xu Zhang, Xin Meng, Jian Zhang, Jiantao Pu · 发表于：Retina · 年份：2019 · DOI：10.1097/iae.0000000000002621 · 被引用次数：34 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments、Retinal and Optic Conditions

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% confid...