Osteoarthritis

Knee joint injuries, such as ligament rupture, are common in athletes. As the intact joint ligaments offer a precondition for joint stability, ligament injuries often surgically reconstructed. However, in many cases these injuries or surgeries can lead to post-traumatic osteoarthritis. The articular cartilage, which serves to provide friction less contact between bones; wears out completely, causing severe joint pain, lack of mobility and even social isolation.

Currently, preventing the onset and development of osteoarthritis; is still the best clinical course of action. Computational modelling used to predict locations susceptible to osteoarthritis; however, are too complicated for clinical use and lack verification of predictions. Researchers from the University of Eastern Finland; in collaboration with the University of California in San Francisco, Kuopio University Hospital, have developed a method to predict post-traumatic osteoarthritis in patients with ligament ruptures; using a simplified computational model.

Computational models generated

The researchers also verified the model predictions against measured structural and compositional changes in the knee joint between follow-up times. The findings reported in Clinical Biomechanics. In this proof-of-concept study, computational models generated from patient clinical magnetic resonance images and measured motion.  Articular cartilage assumed to degenerate due to excessive tissue stresses, leading to collagen fibril degeneration, or excessive deformations, causing proteoglycan loss.

These predictions then compared against changes in MRI-specific parameters linked to each degeneration mechanism. Two patient-specific finite element models of knee joints with anterior cruciate ligament reconstruction were created. The knee geometry was based on clinical magnetic resonance imaging and joint loading was obtained via motion capture. They evaluated biomechanical parameters linked with cartilage degeneration and compared the identified risk areas against T2 and T maps.

Avoiding or delaying osteoarthritis

“The results suggest that a relatively simple finite element model; in terms of geometry, motion and materials, can identify areas susceptible to osteoarthritis, in line with measured changes in the knee joint from MRI. Such methods would be particularly useful in assessing the effect of surgical interventions; or in evaluating non-surgical management options for avoiding or delaying osteoarthritis onset and/or progression,” Researcher Paul Bolcos, a Ph.D. student at the University of Eastern Finland, says.

The findings are significant and could provide pathways for patient-specific clinical evaluation of osteoarthritis risks; and reveal optimal and individual rehabilitation protocols. “They are currently working on adding more patients in order to help tune the degeneration parameters and ensure the sensitivity of the mechanical to MRI parameters. Later, this method could combine with a fully automated approach for generating these computational models; developed in our group, narrowing the gap between research and clinical application,” Bolcos continues.