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

期刊

SURVEY OF OPHTHALMOLOGY
卷 64, 期 4, 页码 498-511

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.survophthal.2019.02.003

关键词

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

资金

  1. Scottish Imaging Network
  2. Platform for Scientific Excellence (SINAPSE) Collaboration
  3. Optos plc.
  4. Welcome Trust
  5. Academy of Medical Sciences
  6. Fight for Sight

向作者/读者索取更多资源

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. (C) 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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