Towards a Transferable Modeling Method of the Knee to Distinguish Between Future Healthy Joints from Osteoarthritic Joints: Data from the Osteoarthritis Initiative

Ann Biomed Eng. 2023 Oct;51(10):2192-2203. doi: 10.1007/s10439-023-03252-8. Epub 2023 Jun 7.

Abstract

Computational models can be used to predict the onset and progression of knee osteoarthritis. Ensuring the transferability of these approaches among computational frameworks is urgent for their reliability. In this work, we assessed the transferability of a template-based modeling strategy, based on the finite element (FE) method, by implementing it on two different FE softwares and comparing their results and conclusions. For that, we simulated the knee joint cartilage biomechanics of 154 knees using healthy baseline conditions and predicted the degeneration that occurred after 8 years of follow-up. For comparisons, we grouped the knees using their Kellgren-Lawrence grade at the 8-year follow-up time and the simulated volume of cartilage tissue that exceeded age-dependent thresholds of maximum principal stress. We considered the medial compartment of the knee in the FE models and used ABAQUS and FEBio FE softwares for simulations. The two FE softwares detected different volumes of overstressed tissue in corresponding knee samples (p < 0.01). However, both programs correctly distinguished between the joints that remained healthy and those that developed severe osteoarthritis after the follow-up (AUC = 0.73). These results indicate that different software implementations of a template-based modeling method similarly classify future knee osteoarthritis grades, motivating further evaluations using simpler cartilage constitutive models and additional studies on the reproducibility of these modeling strategies.

Keywords: Cartilage; Finite element modeling; Knee; Osteoarthritis; Transferability.

MeSH terms

  • Biomechanical Phenomena
  • Cartilage, Articular*
  • Humans
  • Knee Joint
  • Magnetic Resonance Imaging / methods
  • Osteoarthritis, Knee* / diagnosis
  • Reproducibility of Results