Associate Professor Rami Korhonen from the University of Eastern Finland has studied the use of supercomputer modelling to simulate the progression of osteoarthritis of the knee. Korhonen has received funding from the Academy of Finland and the European Research Council (ERC).
“Current imaging methods, such as radiography and magnetic resonance imaging, only provide data on the narrowing of the joint space or on the thickness and characteristics of the joint cartilage. However, assessing the development of arthrosis individually for each patient is a difficult and highly subjective task,” says Korhonen.
The results of Korhonen’s research team were published in the journal Scientific Reports earlier this year. The team developed a novel method based on a computational model that uses magnetic imaging information for the patient-specific assessment of how arthrosis progresses in the knee.
“The method is based on excessive and cumulatively accumulated stresses within the knee joint cartilage during gait loading. The computational simulation relies on finite element modelling. The basic idea is that the cumulative stresses caused by walking causes local degeneration in the knee cartilages of overweight people,” explains Korhonen.
The developed algorithm was tested and validated against an experimental baseline and four-year follow-up data. The algorithm accurately simulated cartilage degeneration in the subject group with overweight people, while the normal-weight subject group’s joints remained intact.
In the future, the method developed by Korhonen’s team may provide a new tool for patient-specific prediction of arthrosis progression. The method could be used, for instance, to evaluate how obesity affects cartilage health or to assess the effects of surgical operations (e.g. ligament reconstruction) on the development of arthrosis. The ultimate goal of the computational model is to find the best possible treatment to slow the progression of arthrosis,” says Korhonen.