Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Journal: Translational vision science & technology
Published Date: Aug 1, 2024

Abstract

PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group.

Authors

  • Alexander Richardson
    Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA.
  • Anita Kundu
    Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA.
  • Ricardo Henao
    Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
  • Terry Lee
    CIHR Canadian HIV Trials Network, Vancouver, British Columbia, Canada.
  • Burton L Scott
    iMIND Research Group, Duke University School of Medicine, Durham, NC, USA.
  • Dilraj S Grewal
    Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA.
  • Sharon Fekrat
    Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA.

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