Machine-to-machine learning may lead to improved grading of disc photos

SAN FRANCISCO — A machine-to-machine method of deep learning may lead to a more accurate way to estimate the presence of glaucoma from photographs, according to a speaker.
“We ophthalmologists know that we perform very poorly in diagnosing glaucoma from photographs. It’s a challenging task unless you have very advanced disease. It’s a challenging task to look at a picture and determine whether the nerve has glaucoma or not,” Felipe A. Medeiros, MD, MPH, of Duke University, said at the American Glaucoma Society annual meeting.
Because ophthalmologists tend to

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