Health
Machine learning algorithms can detect early subtle signs of osteoarthritis
Researchers at the University of Pittsburgh School of Medicine and Carnegie Mellon University have created machine learning algorithms that can detect subtle signs of osteoarthritis-too many to register in the eyes of trained radiologists. I did an MRI scan a year ago and the symptoms start. These results will be published this week PNAS..
This predictive approach allows patients to be treated with prophylactic drugs one day instead of undergoing joint replacement surgery.
The gold standard for diagnosing arthritis is x-rays. As cartilage deteriorates, the space between bones decreases. The problem is that if X-rays show arthritis, the damage has already taken place. It is much easier to keep the cartilage from falling apart than to grow the cartilage again. “
Kenneth Urish, MD, Ph.D. , Associate Professor of Orthopedics at Pitt, Research Author, Deputy Director of Bone and Joint Center, UPMC Magee-Womens Hospital
Currently, the main treatment for osteoarthritis is joint replacement. Also, the condition is so prevalent that knee replacement is the most common in the United States for people over the age of 45.
In this study, researchers examined knee MRI of the osteoarthritis initiative. They focused on a subset of patients with little evidence of cartilage damage at the beginning of the study.
Looking back, we found out which of these participants developed arthritis and which did not. Computers can use that information to learn subtle patterns of MRI scans of presymptomatic people who predict future risk of osteoarthritis.
“When a doctor looks at an image of these cartilage, there is no pattern that pops out with the naked eye, but that does not mean that there is no pattern there. It just means that you cannot see it using conventional tools. The lead author, Dr. Shinji Nikundu, said he completed the project as part of the Pit Medical Scientist Training Program and the graduate training in Carnegie Mellon Biomedical Engineering.
To validate this approach, Kundu, now a resident physician and medical researcher at the Johns Hopkins Department of Radiology, trained the model on a subset of knee MRI data and tested it in a patient never seen before. .. Kundu did this dozens of times, with different participants each time, to test the algorithm on all the data.
Overall, the algorithm predicted osteoarthritis with 78% accuracy from MRI performed 3 years before the onset of symptoms.
Currently, there is no drug to prevent presymptomatic osteoarthritis from developing into full-blown joint deterioration, but it is very effective in preventing patients from developing related conditions (rheumatoid arthritis). There are several medicines.
The goal is to develop the same type of drug for osteoarthritis. Some candidates are already in the preclinical stage.
“Instead of recruiting 10,000 people and following them for 10 years, we only need to enroll 50 people who are known to have osteoarthritis in 2-5 years,” Urish said. “Then we can give them drugs and see if it stops the progression of the disease.”
Source:
Journal reference:
Kundu, S. , other (2020) Transport-based learning enables early detection of osteoarthritis from presymptomatic cartilage texture maps. PNAS. doi.org/10.1073/pnas.1917405117..
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