Research from the Center for Eye Research Australia (CERA) has shown that artificial intelligence (AI) systems can accurately detect and measure unique deposits in the eye that are important in the progression of age-related macular degeneration (AMD).
This discovery opens the possibility of studying symptoms on a scale previously thought impossible.
These deposits – called reticular pseudodrusen (RPD) – have previously been associated with an increased risk of progressing to end-stage AMD.
This deposit is not completely understood, so to learn more about it we need to look at eye scans of many people who have it. However, they are difficult to identify and measure.
“These deposits can be difficult to identify for many clinicians because of the way they appear on scans,” says Associate Professor Zhichao Wu, one of the study’s corresponding authors.
“And to accurately quantify or measure that extent would be prohibitively time-consuming to do manually.
“We would like to do large-scale studies in hundreds or even thousands of people with these deposits to learn more about what they mean for age-related macular degeneration, but we don’t have the people or time to do it manually.”
efficient scanning
The AI model, developed in collaboration with Dr. Himesh Kumar and colleagues at the University of Washington, was trained on hundreds of scans to automatically detect RPD and measure the amount of RPD in the eye.
We then compared the effectiveness of this model with eye care professionals and found that it performed similarly to human expert clinicians.
“This model not only automates the RPD detection process, but also provides an objective way to measure the amount of RPD present,” says Associate Professor Wu.
“This is not a study that can be done by hand and really unlocks the potential to learn more about these deposits more quickly.”
To facilitate this, the team made the model public for other researchers to use in their own studies.
“We want to make this research possible for people around the world,” says Associate Professor Wu.
“The ability to learn more from both Scan and teams around the world brings us closer to a deeper understanding of the role of these deposits at AMD.”
read the research
H. Kumar, Y. Bagdasarova, S. Song, et al., “Deep learning-based detection of reticular pseudodrusen in age-related macular degeneration,” Clinical and Experimental Ophthalmology (2025): 1-8, https://doi.org/10.1111/ceo.14607.
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CERA’s Macular Research Unit studies the link between sleep apnea and age-related macular degeneration.
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