Machine learning algorithm can help predict progression of age-related macular degeneration

Scientists have demonstrated that a machine learning algorithm, using images of patients' retinas obtained over time, can predict the critical moment when early or intermediate age-related macular degeneration (AMD) will progress into severe AMD. The research is being presented during a press conference at the 2017 Annual Meeting of the Association for Research in Vision and Ophthalmology (ARVO) in Baltimore, Md, on Monday, May 8 from 9:30 – 10:15 am.

After gathering optical coherence tomography (OCT) images of 38 patients' retinas with early/intermediate AMD -; every three months for a minimum of 15 months -; researchers used the algorithm to accurately predict the occurrence of drusen regression within the next 12 months. While the presence of lipid/protein deposits called drusen is the hallmark of early/intermediate age-related macular degeneration (AMD), their sudden regression is strongly associated with the onset of late AMD.

Currently, there are no treatments for early/intermediate AMD. Anti-VEGF treatments for late AMD can prevent disease progression, but only after some vision loss has occurred. By pinpointing the moment of transition from early/intermediate to late AMD, the researchers state that machine learning will substantially contribute to the development of new therapeutics that target slowing AMD progression.