A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

Journal: Translational vision science & technology
Published Date: Jul 8, 2022

Abstract

PURPOSE: Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.

Authors

  • Pengxiao Zang
    Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.
  • Tristan T Hormel
    Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
  • Xiaogang Wang
    Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Kotaro Tsuboi
    Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
  • David Huang
    Casey Eye Institute, Oregon Health & Science University, 3375 SW Terwilliger Blvd, Portland, OR 97205, USA.
  • Thomas S Hwang
    Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
  • Yali Jia
    Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.

Keywords

External Resources

Popular Topics
Recent Journals