TY - JOUR
T1 - Automated detection of age-related macular degeneration in color fundus photography
T2 - a systematic review
AU - Pead, Emma
AU - Megaw, Roly
AU - Cameron, James
AU - Fleming, Alan
AU - Dhillon, Baljean
AU - Trucco, Emanuele
AU - MacGillivray, Thomas
N1 - The PhD studentship of E.P. is jointly funded by the Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, and Optos plc. A.F. is employed by Optos plc. R.M. is funded by the Welcome Trust, the Academy of Medical Sciences and Fight for Sight.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
KW - age-related disorders
KW - age-related macular degeneration
KW - artificial intelligence
KW - deep learning
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85065516619&partnerID=8YFLogxK
U2 - 10.1016/j.survophthal.2019.02.003
DO - 10.1016/j.survophthal.2019.02.003
M3 - Article
C2 - 30772363
AN - SCOPUS:85065516619
SN - 0039-6257
VL - 64
SP - 498
EP - 511
JO - Survey of Ophthalmology
JF - Survey of Ophthalmology
IS - 4
ER -