Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.

ABSTRACT: Ocular images play an essential role in ophthalmology. Current research mainly focuses on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the progression of ophthalmic disease. Therefore exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (CNN) to extract high-level features from consecutive slit-lamp images and applying long short term memory (LSTM) method to mine the temporal relationship of features. First, we comprehensively compare six potential combinations of CNNs

SUBMITTER: Jiang J 

PROVIDER: S-EPMC6067742 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature