A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs Sidong Liu, PhD, Stuart L. Graham, FRANZCO, PhD, Angela.

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A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs Sidong Liu, PhD, Stuart L. Graham, FRANZCO, PhD, Angela Schulz, PhD, Michael Kalloniatis, PhD, Barbara Zangerl, PhD, DVM, Weidong Cai, PhD, Yang Gao, PhD, Brian Chua, FRANZCO, Hemamalini Arvind, FRANZCO, PhD, John Grigg, MD, FRANZCO, Dewei Chu, PhD, Alexander Klistorner, MD, PhD, Yuyi You, MD, PhD Ophthalmology Glaucoma Volume 1, Issue 1, Pages 15-22 (July 2018) DOI: 10.1016/j.ogla.2018.04.002 Copyright © 2018 American Academy of Ophthalmology Terms and Conditions

Figure 1 Difference-of-Gaussian-based optic disc detection. Multiple Gaussian filters with different kernel sizes were applied to the input image to construct the Gaussian scale-space of the image. The optic disc then can be identified by searching the local maxima in the scale-space. Ophthalmology Glaucoma 2018 1, 15-22DOI: (10.1016/j.ogla.2018.04.002) Copyright © 2018 American Academy of Ophthalmology Terms and Conditions

Figure 2 The ResNet50 architecture with 50 layers in the neural network. The hidden layers in the middle were grouped into convolutional blocks (Conv Block) and identify blocks (ID Block) to avoid showing the repeated layers and to visualize this network in a concise way. Avg = average; conv = convolutional; FC = fully connected; relu = rectified linear units. Ophthalmology Glaucoma 2018 1, 15-22DOI: (10.1016/j.ogla.2018.04.002) Copyright © 2018 American Academy of Ophthalmology Terms and Conditions

Figure 3 Representative input images and their corresponding saliency maps detected by the artificial intelligence system: (A) normal, (B) advanced glaucoma, (C) glaucoma with disc margin hemorrhage, and (D) glaucoma with peripapillary atrophic crescent. Ophthalmology Glaucoma 2018 1, 15-22DOI: (10.1016/j.ogla.2018.04.002) Copyright © 2018 American Academy of Ophthalmology Terms and Conditions

Figure 4 Graph showing a comparison of the performance between the artificial intelligence (AI) system (blue curve) and ophthalmologists (green, general ophthalmologists; red, glaucoma subspecialists) on random sets of 80 images obtained from 800 testing images. Each point represents a test performed by an ophthalmologist. AUC = area under the receiver operating characteristic curve; ROC = receiver operating characteristic. Ophthalmology Glaucoma 2018 1, 15-22DOI: (10.1016/j.ogla.2018.04.002) Copyright © 2018 American Academy of Ophthalmology Terms and Conditions

Figure 5 Graph showing a comparison of the performance of the artificial intelligence (AI) system (blue curve) and ophthalmologists (red dots) on 30 images from the publicly available High-Resolution Fundus online database. AUC = area under the receiver operating characteristic curve; ROC = receiver operating characteristic. Ophthalmology Glaucoma 2018 1, 15-22DOI: (10.1016/j.ogla.2018.04.002) Copyright © 2018 American Academy of Ophthalmology Terms and Conditions