January 2021

Machine Learning Detects Early Signs of OA

The cartilage in this MRI scan of a knee is colorized to show greater contrast between shades of gray. Image courtesy of Kundu et al.

A research team led by Shinjini Kundu, MD, PhD, of Carnegie Mellon University and the University of Pittsburgh (Pitt) and Gustavo Rohde, PhD, of the University of Virginia, investigated whether artificial intelligence could be used to analyze MRI images for early signs of osteoarthritis (OA) and predict who will develop the disease. They used MRI scans from 86 people who had no initial symptoms or visual signs of disease. About half the participants developed OA after 3years.

The team developed a technique called 3-dimensional transport-based morphometry (3D TBM) to identify biochemical changes, such as how much water is present, in cartilage using MRI scans. Using 3D TBM, they analyzed baseline “cartilage maps” of the participants’ knees. After 3 years, they compared the cartilage maps for the participants who were eventually diagnosed with OA with those who were not.

Using machine-learning algorithms, the team trained the system to automatically differentiate between people who would progress to OA and those who wouldn’t. The technique detected specific biochemical changes in the center of the knee’s cartilage of those who were pre-symptomatic at the time of the baseline imaging, including decreases in water concentration. The system accurately detected 78% of future OA cases.

“The gold standard for diagnosing arthritis is X-ray,” said research team member Kenneth Urish, MD, PhD, of Pitt. “As the cartilage deteriorates, the space between the bones decreases. The problem is, when you see arthritis on X-rays, the damage has already been done.”

More studies are needed to determine whether 3D TBM could be useful as a clinical tool to predict who may develop OA and benefit from early interventions.

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