February 2022
Volume 22, Issue 3
Open Access
Optica Fall Vision Meeting Abstract  |   February 2022
Invited Session I: Artificial intelligence applications in ophthalmology and vision science: Deep learning applications in clinical ophthalmology
Author Affiliations
  • Cecilia S. Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
    Karalis Johnson Retina Center, Seattle, Washington, United States
Journal of Vision February 2022, Vol.22, 40. doi:https://doi.org/10.1167/jov.22.3.40
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      Cecilia S. Lee; Invited Session I: Artificial intelligence applications in ophthalmology and vision science: Deep learning applications in clinical ophthalmology. Journal of Vision 2022;22(3):40. doi: https://doi.org/10.1167/jov.22.3.40.

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

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Abstract

Rapid advances in retinal imaging technology combined with deep learning approaches for image analysis have provided new avenues of investigation in ophthalmic disease. First, deep learning provides a de novo approach to image analysis, identifying previously unrecognized imaging features that correlate with functional changes. In age-related macular degeneration (AMD), deep learning approaches identified subtle retinal features, hyporeflective outer retinal bands in the central macula, that are associated with delayed rod-mediation dark adaptation, a functional biomarker of early AMD. Second, deep learning allows prediction of clinical outcomes such as visual field progression in glaucoma. Lastly, deep learning models can also be used to segment anatomic features from ophthalmic imaging, enabling accurate and fully automated periorbital measurements with many potential clinical applications in oculoplastics. Deep learning applications in ophthalmic imaging have potential to improve our understanding of disease and their clinical outcomes.

Footnotes
 Funding: Unrestricted grant RPB, NIA/NIH U19AG066567, NIA/NIH R01AG060942, Klorfine Family Endowed Chair
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