July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
A deep learning system for detecting glaucomatous optic neuropathy and age-related macular degeneration based on color fundus photographs
Author Affiliations & Notes
  • Mingguang He
    Zhongshan Ophthalmic Center, Guangzhou, China
    University of Melbourne, Melbourne, Victoria, Australia
  • Zhixi Li
    Zhongshan Ophthalmic Center, Guangzhou, China
  • Stuart Keel
    University of Melbourne, Melbourne, Victoria, Australia
  • Robert Chang
    Byers Eye Institute at Stanford University , Palo Alto, California, United States
  • Footnotes
    Commercial Relationships   Mingguang He, None; Zhixi Li, None; Stuart Keel, None; Robert Chang, None
  • Footnotes
    Support  Supported in part by the Fundamental Research Funds of the State Key Laboratory in Ophthalmology, National Natural Science Foundation of China (81420108008) and Science and Technology Planning Project of Guangdong Province (2013B20400003). Prof. Mingguang He receives support from the University of Melbourne at Research Accelerator Program and the CERA Foundation. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organization had no role in the design or conduct of this research.
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 2086. doi:
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    • Get Citation

      Mingguang He, Zhixi Li, Stuart Keel, Robert Chang; A deep learning system for detecting glaucomatous optic neuropathy and age-related macular degeneration based on color fundus photographs. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2086.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) and late wet age-related macular degeneration (LW-AMD) based on color fundus photographs.

Methods : A deep learning system for automated classification of GON and LW-AMD was developed based on more than 50,000 fundus photographs. A total of 21 trained ophthalmologists were invited to label all these images using an online cloud source system. Referable GON was defined as vertical cup/disc ratio ≥ 0.7 and other typical changes of GON. AMD was classified as absent, early, intermediate, late dry and late wet subtypes. The reference standard was made when ≥3 graders had achieved agreement. A separate validation dataset of 8,000 fully gradable fundus photographs was used to assess the performance of this algorithm.

Results : In the validation dataset, this deep learning system achieved an AUC of 0.986 with sensitivity of 95.6% and specificity of 92.0% for GON; these results were 0.995, 98.0% and 94.0% for LW-AMD, respectively. The most common reasons for false negative grading (n=87) for GON were co-existing eye conditions (n=44, 50.6%) while the reason for false positive cases (n=480) was having other eye conditions (n=458, 95.4%), mainly including physiologic cupping (n=267, 55.6%). For the misclassification of LW-AMD, serous detachment of the sensory retina or retinal pigment epithelium (n=8, 57.2%) were identified as the most common features in the false negative classification. Images with other eye diseases (n=145, 76.3%), such as diabetic retinopathy (n=38, 20%), myopic maculopathy (n=22, 11.5%), early or intermediate AMD (n=21, 11.1%) and choroiditis (n=12, 6.3%), were the commonest features for false positive classification.

Conclusions : A deep learning system can detect referable GON and LW-AMD with high sensitivity and specificity. The algorithm should be further validated among the images collected in real-world clinical practice.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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