Researchers at the Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, Maryland, and collaborators at the Johns Hopkins School of Medicine, have developed image analysis and machine learning tools to detect AMD.

In a recent article in Nature Medicine, members of the team discuss the potential of such tools to be used clinically and applied to other image-based medical diagnoses as well.

In 2015, the APL’s Dr Philippe Burlina, PhD, a co-principal investigator for the project, and colleagues teamed up with the Johns Hopkins Wilmer Eye Institute on ways to automate AMD diagnosis.

In published papers in the JAMA, they demonstrated that machine diagnostics using deep learning can match the performance of human ophthalmologists.

The team has also expanded its inquiry to characterise retinal layers as seen on OCT.