Automated detection of age-related macular degeneration in color fundus photography: a systematic review

Emma Pead, Roly Megaw, James Cameron, Alan Fleming, Baljean Dhillon, Emanuele Trucco, Thomas MacGillivray

Research output: Contribution to journalArticle

2 Citations (Scopus)
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The rising prevalence of age-related eye diseases, particularly age-related macular degeneration, places an ever-increasing burden on health care providers. As new treatments emerge, it is necessary to develop methods for reliably assessing patients' disease status and stratifying risk of progression. The presence of drusen in the retina represents a key early feature in which size, number, and morphology are thought to correlate significantly with the risk of progression to sight-threatening age-related macular degeneration. Manual labeling of drusen on color fundus photographs by a human is labor intensive and is where automatic computerized detection would appreciably aid patient care. We review and evaluate current artificial intelligence methods and developments for the automated detection of drusen in the context of age-related macular degeneration.

Original languageEnglish
Pages (from-to)498-511
Number of pages14
JournalSurvey of Ophthalmology
Issue number4
Early online date24 Feb 2019
Publication statusPublished - 1 Jul 2019



  • age-related disorders
  • age-related macular degeneration
  • artificial intelligence
  • deep learning
  • machine learning

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