Novel Computational Model Simulates Long-Term Osteoarthritic Risk - European Medical Journal

Novel Computational Model Simulates Long-Term Osteoarthritic Risk

2 Mins

DEGENERATION rates of joint cartilage over time have been accurately predicted using a newly developed computer simulation model, researchers have revealed in a recent study.

Techniques such as magnetic resonance and X-ray imaging are common in current medical practice, and they are useful for measuring the thickness and composition of knee joint cartilage in osteoarthritic patients; they fail, however, to provide insight into the patient-specific risk of the disease and its potential progression. A degenerative disease, osteoarthritis involves the gradual wearing of the articular cartilage over time. This degeneration can be exacerbated by weight-induced stress or older age, both significant risk factors for the disease.

Through the use of magnetic resonance imaging data, a team have built a computational model that analyses the data to provide patient-specific predictions of osteoarthritic progression. Based on the understanding that stress built up over time, induced by activities such as walking, will initiate degeneration beyond a relative threshold, this risk model was able to estimate individual patient changes in articular cartilage.

In order to understand the accuracy of these predictions, the analyses were compared with data acquired at 4-year follow-up in a normal weight and an overweight group. Using the Kellgren–Lawrence grading scale, articular cartilage thickness was measured from X-ray images, demonstrating significant degeneration of cartilage in the overweight group compared with no changes to healthy cartilage in the normal weight group. “The study shows that this new method, which is based on computer modelling, was able to predict similar changes in the articular cartilage of the knee as experimental follow-up data,” explained Dr Mika Mononen, Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.

The team envision a multifunctional application of this technology in the diagnosis and management of osteoarthritis, as the model may also be useful for evaluating the potential success of surgical treatments such as meniscectomy, and the future health of knee joints in certain overweight patients.


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