An artificial intelligence algorithm can detect subtle signs of osteoarthritis in MRI scans, years before symptoms of the condition even begin.
Researchers at University of Pittsburgh School of Medicine and Carnegie Mellon University College of Engineering noted that right now, the primary treatment for osteoarthritis is joint replacement. The condition is so prevalent that knee replacement is the most common surgery in the US for people over the age of 45.
“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, MD, PhD, 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.”
For the study, researchers looked at knee MRIs from the Osteoarthritis Initiative, a project that followed thousands of people for seven years to see how osteoarthritis of the knee develops. The team focused on a group of patients who had little evidence of cartilage damage at the beginning of the study.
The team used this dataset to train an AI algorithm to learn subtle patterns on the MRI and determine which patients would go on to develop osteoarthritis and which wouldn’t.
“When doctors look at these images of the cartilage, there isn’t a pattern that jumps out to the naked eye, but that doesn’t mean there’s not a pattern there. It just means you can’t see it using conventional tools,” said lead author Shinjini Kundu, MD, PhD, who completed this project as part of her graduate training in the Pitt Medical Scientist Training Program and Carnegie Mellon Department of Biomedical Engineering.
To validate this approach, researchers tested the model on MRIs of patients it had never seen before. The team repeated the process dozens of times, withholding different participants each time in order to test the algorithm on all the data.
The results showed that overall, the algorithm predicted osteoarthritis with 78 percent accuracy from MRIs performed three years before symptom onset.
Currently, there are no drugs preventing pre-symptomatic osteoarthritis from developing into full-blown joint deterioration, although there are a few highly effective drugs that can prevent patients from developing rheumatoid arthritis, a related condition. The ultimate goal is to develop the same types of drugs for osteoarthritis, and several candidates are already in the preclinical pipeline.
With this new AI approach, patients could one day be treated with preventative drugs rather than undergoing joint replacement surgery.
“Instead of recruiting 10,000 people and following them for ten years, we can just enroll 50 people who we know are going to be getting osteoarthritis in two or five years,” Urish said. “Then we can give them the experimental drug and see whether it stops the disease from developing.”
Researchers have increasingly sought to develop AI and other advanced analytics tools to deliver more preventative care for patients in need. In 2019, researchers developed an artificial intelligence tool that could enable non-ophthalmologists to accurately detect diabetic retinopathy in 60 seconds, making real-time screening possible for primary care organizations and diabetes centers.
The system accurately identified eye disease in patients with diabetes 95.5 percent of the time, which could improve care for millions of patients who need yearly screenings for diabetic retinopathy.
“Diabetic patients already outnumber practicing ophthalmologists in the United States, and unfortunately, that imbalance is only expected to grow,” said Srinivas Sadda, MD of the Doheny Eye Institute/UCLA.
“Accurate, real-time diagnosis holds great promise for the millions of patients living with diabetes. In addition to increased accessibility, a prompt diagnosis made possible with AI means identifying those at risk of blindness and getting them in front of an ophthalmologist for treatment before it is too late.”