Optimized simplified constitutive models with an atlas-based finite element analysis can be utilized to predict personalized progression of knee osteoarthritis:  Data from the Osteoarthritis Initiative

Abstract

New technologies are required to support a radical shift towards preventive healthcare. Here we focus on evaluating the possibility of finite element (FE) analysis-aided prevention of knee osteoarthritis (OA), a disease that affects 100 million citizens in the US and EU and this number is estimated to increase drastically. Current clinical methods to diagnose or predict joint health status relies on symptoms and tissue failures obtained from clinical imaging. In a joint with no detectable injuries, the diagnosis of the future health of the knee can be assumed to be very subjective. Quantitative approaches are therefore needed to assess the personalized risk for the onset and development of knee OA. FE analysis utilizing an atlas-based modeling approach has shown a preliminary capability for simulating subject-specific cartilage mechanical responses. However, it has been verified with a very limited subject number. Thus, the aim of this study is to verify the real capability of the atlas-based approach to simulate cartilage degeneration utilizing different material descriptions for cartilage. A fibril reinforced poroviscoelastic (FRPVE) material formulation was considered as state-of-the-art material behavior and simulated mechanical tissue responses and predicted cartilage degenerations within knee joint were compared against to simpler constitutive models for cartilage. The capability of the atlas-based modeling to offer a feasible approach with quantitative evaluation for the risk for the OA development (healthy vs osteoarthritic knee, p < 0.01, AUC ~ 0.7) was verified with 214 knees. Furthermore, the results suggest that accuracy for simulation of cartilage degeneration with simpler material models is similar to models using FPRVE materials if the material parameters are chosen properly.

An Automated and Robust Tool for Musculoskeletal and Finite Element Modeling of the Knee Joint

“…This implies a consistent prediction of cartilage degradation using either template-based or auto-meshing pipelines from the MRI sequences tested in the current study. Our previous studies have also demonstrated comparable cartilage mechanics and degradation between template-based and manual FE modeling [21], [82]. Results of the current study suggest that the template-based method is viable, particularly because it showed no failures to create MSK and FE models and had substantially fewer convergence issues than the auto-meshing method.…”

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

“…To better understand the progression of osteoarthritis, numerous studies have employed computer simulation to explore the knee from the mechanical [44][45][46] and biochemical [47][48][49] perspectives. However, none of them have explicitly addressed the simulation of the electrical properties of the synovial fluid or the cartilage, which could provide valuable insight in understanding the physiopathology of the disease.…”