Journal article
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs
Z Li, Y He, S Keel, W Meng, RT Chang, M He
Ophthalmology | ELSEVIER SCIENCE INC | Published : 2018
Abstract
Purpose: To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus photographs. Design: A deep learning system for the classification of GON was developed for automated classification of GON on color fundus photographs. Participants: We retrospectively included 48 116 fundus photographs for the development and validation of a deep learning algorithm. Methods: This study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON. The reference standard was made until 3 graders achieved agreement. A separa..
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Awarded by Research to Prevent Blindness
Funding Acknowledgements
Supported in part by the Fundamental Research Funds of the State Key Laboratory in Ophthalmology, National Natural Science Foundation of China (grant no.: 81420108008); the Science and Technology Planning Project of Guangdong Province (grant no.: 2013B20400003); the University of Melbourne at Research Accelerator Program of Australia (M.H.); the CERA Foundation of Australia (M.H.); Victorian State Government of Australia (Operational Infrastructure Support to the Centre for Eye Research Australia); and Research to Prevent Blindness, Inc., New York (to Stanford University Eye Department). The sponsors or funding organizations had no role in the design or conduct of this research.