Health Life

Artificial intelligence detects osteoarthritis years before it develops

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Researchers at the University of Pittsburgh School of Medicine and Carnegie Mellon University College of Engineering have created a machine-learning algorithm that can detect subtle signs of osteoarthritis—too abstract to register in the eye of a trained radiologist—on an MRI scan taken years before symptoms even begin. These results will publish this week in PNAS.

With this predictive approach, patients could one day be treated with preventative drugs rather than undergoing .

“The gold standard for diagnosing arthritis is X-ray. As the cartilage deteriorates, the space between the bones decreases,” said study co-author Kenneth Urish, M.D., Ph.D., associate professor of orthopaedic surgery at Pitt and associate medical director of the bone and joint center at UPMC Magee-Womens Hospital. “The problem is, when you see arthritis on X-rays, the damage has already been done. It’s much easier to prevent cartilage from falling apart than trying to get it to grow again.”

Right now, the primary treatment for osteoarthritis is joint replacement. And the condition is so prevalent that knee replacement is the most common surgery in the U.S. for people over age 45.

For this study, the researchers looked at knee MRIs from the Osteoarthritis Initiative, which followed thousands of people for seven years to see how osteoarthritis of the knee develops. They focused on a subset of patients who had little evidence of cartilage damage at the beginning of the study.

In retrospect, we now know which of these participants went on to develop arthritis and which didn’t, and the computer can use that information to learn subtle patterns on the MRI scans of presymptomatic people that are predictive of their future osteoarthritis risk.

“When doctors look at these images of the cartilage, there isn’t a pattern that jumps out to the