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Invited Commentary
May 13, 2021

Automated Retinal Fluid Volume Quantification: A Nod to Present and Future Applications of Deep Learning

Author Affiliations
  • 1Department of Ophthalmology, University of Washington, Seattle
JAMA Ophthalmol. 2021;139(7):741-742. doi:10.1001/jamaophthalmol.2021.1284

The chronicles of artificial intelligence (AI) in ophthalmology began circa 2017 in most clinicians’ minds, with multiple studies demonstrating comparable performance of deep learning algorithms with human experts in diabetic retinopathy (DR) classification using fundus photographs.1-3 The momentum around this work has led to important AI research in other ophthalmic diseases—notably, glaucoma, age-related macular degeneration, and retinopathy of prematurity4—but DR remains an archetypal training ground on which innovation in AI is studied. With an increasing worldwide population with diabetes threatening to overwhelm limited human screening resources, higher-throughput DR screening has become an undisputed public health need. The routine acquisition of retinal imaging in the quotidian diagnosis and management of DR, in the context of recent advances in machine learning techniques and computer processing power to implement such techniques, positions DR screening perfectly for computer-assisted image analysis and classification.

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