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    Artificial intelligence will bring ‘reality’ to glaucoma diagnosis

    Could a computer one day beat the best ophthalmologists in diagnosing glaucoma? Not soon, but that day may be just around the corner.

    Computers already using artificial intelligence (AI) have equaled dermatologists in diagnosing skin cancer and nearly matched retina specialists in recognizing diabetic retinopathy from fundus images.

    In his presentation, “New Innovations in Hacking Glaucoma,” during the Glaucoma Symposium at the 2017 Glaucoma 360 meeting, Robert Chang, MD, an assistant professor of ophthalmology, Stanford University, outlined how the technology might work. Dr. Chang is developing AI for glaucoma.

    The technology has made startling strides in recent years, Dr. Chan said. He pointed to the recent success of a computer program in beating champion poker players.

    “We thought with missing information and bluffing it would be very hard for a machine to beat the world’s best poker players,” Dr. Chang said. “AI didn’t just beat the players, it crushed them. This is happening in every gaming field.”

    Power and data grow

    The success of the programs stems from rapid growth in computing power and the availability of massive amounts of data, said Dr. Chang. In the past, most AI programs used a pattern recognition technique in which programmers identified key features of the object to be identified and programmed computers to look for these features. But this approach proved cumbersome.

    “As deep learning came about, it was decided that you didn’t have to identify what were the key features, you just needed enough training examples,” Dr. Chang explained. “Then, the machine would be able to identify statistically what were those key features.

    “It doesn’t stop there,” he added. “You can keep feeding the algorithm more and more data so it can get more accurate.”

    For example, in the past, a programmer might have taught a computer to recognize an Audi A7 by identifying a combination of vertical and horizontal lines unique to that model of car. Now programmers give computers thousands of photographs and label the Audi A7s among them, Dr. Chang said.

    The computer itself picks out distinguishing characteristics and tests them to see how many of the Audi A7s have those features. It may find features in this way that a human would not have noticed. This is more similar to the way human beings think, he said.

    First attempts in 1950s

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