Abstract
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 language | English |
|---|---|
| Pages (from-to) | 498-511 |
| Number of pages | 14 |
| Journal | Survey of Ophthalmology |
| Volume | 64 |
| Issue number | 4 |
| Early online date | 24 Feb 2019 |
| DOIs | |
| Publication status | Published - 1 Jul 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- age-related disorders
- age-related macular degeneration
- artificial intelligence
- deep learning
- machine learning
ASJC Scopus subject areas
- Ophthalmology
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