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

• Published in: Survey of ophthalmology Vol. 64; no. 4; pp. 498 - 511
• Main Authors: Pead, Emma, Megaw, Roly, Cameron, James, Fleming, Alan, Dhillon, Baljean, Trucco, Emanuele, MacGillivray, Thomas
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-07-2019; Elsevier Science
• Subjects: age-related disorders; age-related macular degeneration; Algorithms; artificial intelligence; deep learning; Diagnostic Techniques, Ophthalmological; Humans; Image Processing, Computer-Assisted - methods; machine learning; Macular Degeneration - diagnosis; Photography - methods; Retinal Drusen - diagnosis; deep learning; machine learning; age-related macular degeneration; artificial intelligence; age-related disorders
• ISSN: 0039-6257; 1879-3304
• DOI: 10.1016/j.survophthal.2019.02.003

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.