Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

Journal: Computers in biology and medicine
Published Date: Jan 27, 2017

Abstract

BACKGROUND: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner.

Authors

  • Philippe Burlina
    Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
  • Katia D Pacheco
    Retina Division, Brazilian Center of Vision Eye Hospital, DF, Brazil.
  • Neil Joshi
    Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
  • David E Freund
    Applied Physics Laboratory, The Johns Hopkins University, MD, USA. Electronic address: David.Freund@jhuapl.edu.
  • Neil M Bressler
    Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland4Editor, JAMA Ophthalmology.

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