Investigative Ophthalmology & Visual Science Cover Image for Volume 60, Issue 9
July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Using AI to infer function from structure in retinal imaging
Author Affiliations & Notes
  • Aaron Y Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Aaron Lee, Carl Zeiss Meditec Inc, (F), NVIDIA Corportation (F), Topcon Corporation (F)
  • Footnotes
    Support  NEI K23EY029246, RPB unrestricted core grant
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1409. doi:
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      Aaron Y Lee; Using AI to infer function from structure in retinal imaging. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1409.

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

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Abstract

Presentation Description : Artificial intelligence (AI) has made a tremendous impact in ophthalmology, solving many computer vision problems. However, many successful applications have been limited to classification and feature segmentation. This session will focus on exploring the boundary between anatomic structure and retinal function using AI algorithms by using a different class of AI models. We describe our recent successes in applying these classes of models on OCT imaging and color fundus photographs of the retina.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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