July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
The Development and Validation of a Deep Learning Algorithm for the Detection of Neovascular Age-Related Macular Degeneration from Color Fundus Photographs
Author Affiliations & Notes
  • Stuart Keel
    Centre for Eye Research Australia, Melbourne, Victoria, Australia
  • Zhixi Le
    Zhongshan Ophthalmic Center, China
  • Jane Scheetz
    Centre for Eye Research Australia, Melbourne, Victoria, Australia
  • Mingguang He
    Centre for Eye Research Australia, Melbourne, Victoria, Australia
    Zhongshan Ophthalmic Center, China
  • Footnotes
    Commercial Relationships   Stuart Keel, None; Zhixi Le, None; Jane Scheetz, None; Mingguang He, ZL201510758675.5 (P)
  • Footnotes
    Support  Fundamental Research Funds of the State Key Laboratory in Ophthalmology, National Natural Science Foundation of China (81420108008), Bupa Health Foundation Australia grant
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1358. doi:https://doi.org/
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      Stuart Keel, Zhixi Le, Jane Scheetz, Mingguang He; The Development and Validation of a Deep Learning Algorithm for the Detection of Neovascular Age-Related Macular Degeneration from Color Fundus Photographs. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1358. doi: https://doi.org/.

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

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Abstract

Purpose : To describe the development and validation of a deep learning algorithm (DLA) for the detection of neovascular age-related macular degeneration (AMD).

Methods : A deep learning system for the automated detection of neovascular AMD was developed and validated on a set of 142,275 non-stereoscopic, retinal images (n=4,188 with neovascular AMD). Retinal photographs included in the development and internal validation datasets (n=56,113) were derived from a variety of clinical settings in China. Gold standard grading of these images was assigned when consensus was reached by 3 individual ophthalmologists. For external validation, we tested the efficiency and diagnostic accuracy of the DLA on 86,162 non-mydriatic images (76% Anglo-Celtic origin) derived from Melbourne Collaborative Cohort Study (MCCS). Images from the MCCS were labelled by professional graders. Referable AMD was defined as neovascular AMD and/or ungradable outcome in one or both eyes. Area under the receiver operating characteristic curve (AUC), sensitivity and specificity.

Results : In the internal validation dataset, the AUC, sensitivity and specificity of the DLA for neovascular AMD was 0.995, 96.7%, 96.4%, respectively. Testing against the independent external dataset achieved an AUC, sensitivity and specificity of 0.967, 100% and 93.4%, respectively. More than 60% of false positive cases in the internal and external validation datasets displayed other macular pathologies. Among the false negative cases (internal validation dataset only), over half (57.2%) proved to be undetected detachment of the neurosensory retina or RPE layer.

Conclusions : This DLA shows robust performance for the detection of neovascular AMD amongst retinal images from a multi-ethnic sample and under different imaging protocols. Future efforts will focus on investigating the effectiveness of our deep learning algorithm for neovascular AMD as a screening tool in high risk populations, and what impact the introduction OCT imaging has as a second-line screening tool amongst screen-positive patients.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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