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Abstract #78268 Published in IGR 19-4

Deep learning in ophthalmology: a review

Grewal PS; Oloumi F; Rubin U; Tennant MTS
Canadian Journal of Ophthalmology 2018; 53: 309-313


Deep learning is an emerging technology with numerous potential applications in Ophthalmology. Deep learning tools have been applied to different diagnostic modalities including digital photographs, optical coherence tomography, and visual fields. These tools have demonstrated utility in assessment of various disease processes including cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy. Deep learning techniques are evolving rapidly, and will become more integrated into ophthalmic care. This article reviews the current evidence for deep learning in ophthalmology, and discusses future applications, as well as potential drawbacks.

Department of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, Alta.

Full article

Classification:

6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
15 Miscellaneous



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