No abstract

We present a novel algorithm combined with computational modeling to simulate the development of knee osteoarthritis. The degeneration algorithm was based on excessive and cumulatively accumulated stresses within knee joint cartilage during physiological gait loading. In the algorithm, the collagen network stiffness of cartilage was reduced iteratively if excessive maximum principal stresses were observed. The developed algorithm was tested and validated against experimental baseline and 4-year follow-up Kellgren-Lawrence grades, indicating different levels of cartilage degeneration at the tibiofemoral contact region. Test groups consisted of normal weight and obese subjects with the same gender and similar age and height without osteoarthritic changes. The algorithm accurately simulated cartilage degeneration as compared to the Kellgren-Lawrence findings in the subject group with excess weight, while the healthy subject group’s joint remained intact. Furthermore, the developed algorithm followed the experimentally found trend of cartilage degeneration in the obese group (R2 = 0.95, p < 0.05; experiments vs. model), in which the rapid degeneration immediately after initiation of osteoarthritis (0–2 years, p < 0.001) was followed by a slow or negligible degeneration (2–4 years, p > 0.05). The proposed algorithm revealed a great potential to objectively simulate the progression of knee osteoarthritis.

Focal cartilage lesions can proceed to severe osteoarthritis or remain unaltered even for years. A method to identify high risk defects would be of utmost importance to guide clinical decision making and to identify the patients that are at the highest risk for the onset and progression of osteoarthritis. Based on cone beam computed tomography arthrography, we present a novel computational model for evaluating changes in local mechanical responses around cartilage defects. Our model, based on data obtained from a human knee in vivo, demonstrated that the most substantial alterations around the defect, as compared to the intact tissue, were observed in minimum principal (compressive) strains and shear strains. Both strain values experienced up to 3-fold increase, exceeding levels previously associated with chondrocyte apoptosis and failure of collagen crosslinks. Furthermore, defects at the central regions of medial tibial cartilage with direct cartilage-cartilage contact were the most vulnerable to loading. Also locations under the meniscus experienced substantially increased minimum principal strains. We suggest that during knee joint loading particularly minimum principal and shear strains are increased above tissue failure limits around cartilage defects which might lead to osteoarthritis. However, this increase in strains is highly location-specific on the joint surface.

Currently, there are no clinically available tools or applications which could predict osteoarthritis development. Some computational models have been presented to simulate cartilage degeneration, but they are not clinically feasible due to time required to build subject-specific knee models. Therefore, the objective of this study was to develop a template-based modeling method for rapid prediction of knee joint cartilage degeneration. Knee joint models for 21 subjects were constructed with two different template approaches (multiple templates and one template) based on the MRI data. Geometries were also generated by manual segmentation. Evaluated volumes of cartilage degeneration for each subject, as assessed with the degeneration algorithm, were compared with experimentally observed 4 year follow up Kellgren-Lawrence (KL) grades. Furthermore, the effect of meniscus was tested by generating models with subjectspecific meniscal supporting forces and those with the average meniscal supporting force from all models. All tested models were able to predict most severe cartilage degeneration to those subjects who had the highest KL grade after 4 year follow up. Surprisingly, in terms of statistical significance, the best result was obtained with one template approach and average meniscal support. This model was fully able to categorize all subjects to their experimentally defined groups (KL0, KL2 and KL3) based on the 4 year follow-up data. The results suggest that a template-or population-based approach, which is much faster than fully subject-specific, could be applied as a clinical prediction tool for osteoarthritis.