Abstract
The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fundus photographs (n = 14,822) from a racially and ethnically diverse group of individuals (over 33% of African descent) was evaluated by expert reviewers and classified as GON or healthy. Several deep learning architectures and the impact of transfer learning were evaluated. The best performing model achieved an overall area under receiver operating characteristic (AUC) of 0.91 in distinguishing GON eyes from healthy eyes. It also achieved an AUC of 0.97 for identifying GON eyes with moderate-to-severe functional loss and 0.89 for GON eyes with mild functional loss. A sensitivity of 88% at a set 95% specificity was achieved in detecting moderate-to-severe GON. In all cases, transfer improved performance and reduced training time. Model visualizations indicate that these deep learning models relied on, in part, anatomical features in the inferior and superior regions of the optic disc, areas commonly used by clinicians to diagnose GON. The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs.
Highlights
Clinicians have examined the ONH with ophthalmoscopy and fundus photography to diagnose and monitor glaucoma
Using a large database of ONH-centered fundus images, we evaluated several different convolutional neural networks (CNN) architectures based on glaucoma diagnostic accuracy
Our results suggest that deep learning methodologies have high diagnostic accuracy for identifying fundus photographs with glaucomatous damage to the ONH in a racially and ethnically diverse population
Summary
Clinicians have examined the ONH with ophthalmoscopy and fundus photography to diagnose and monitor glaucoma. Automated methods that use techniques from artificial intelligence to objectively interpret images of the ONH and surrounding fundus can help address this issue. These automated methods may be used as part of decision support systems in clinical management of glaucoma through incorporation into fundus cameras, electronic medical record systems, or picture archiving and communication systems (PACS). Deep convolutional neural networks (CNN) have found use in ophthalmology for tasks that include identifying diabetic retinopathy in fundus images, interpreting and segmenting optical coherence tomography (OCT) images, and detecting drusen, neovascularization, and macular edema by OCT16–19. We quantitatively evaluated the effect of using transfer learning in training these networks
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