Abstract As the prevalence of knee osteoarthritis continues to rise, there is an urgent clinical need for load‐bearing implants that can be arthroscopically delivered to restore joint function and delay major surgical intervention. This study addresses the core scientific challenges of material selection and mechanical characterisation for such implants, focusing on two candidate bioelastomers: polycarbonate urethane (PCU) (ChronoFlex C™ 80A) and silicone‐polycarbonate copolymer (Si‐PC) (ChronoSil® 93A). The novelty of this work lies in combining experimental hyperelastic material model generation for these candidate implant elastomers with a multiphysics computational assessment of a novel arthroscopically insertable inflatable knee implant for load‐bearing osteoarthritis treatment. Comprehensive mechanical testing, including uniaxial tension, compression, planar shear and volumetric compression, was performed to quantify the nonlinear elastic behaviour of the two materials. Multiple hyperelastic material models, namely neo‐Hookean, Mooney–Rivlin, Ogden, Ogden third order and polynomial third order, were fitted to the experimental data, and their accuracy was assessed through numerical modelling. For PCU, the neo‐Hookean and Mooney–Rivlin models exhibited the lowest mean absolute errors, while the Ogden model showed improved performance in the working strain range (−50% to 100%). For Si‐PC, only the neo‐Hookean model fell below 10% error. These validated material models were then incorporated into a multiphysics computational framework to simulate the mechanical behaviour of a novel inflatable knee implant under physiological load, demonstrating acceptable structural response and favourable stress profiles. The computational model included knee joint structures derived from magnetic resonance imaging and computed tomography scans, hyperelastic material models, time‐dependent loading and fluid–structure interactions between the implant and fluid contained within it. The integration of rigorous experimental characterisation and advanced material modelling provides a robust foundation for the future computational optimisation and clinical implementation of soft knee implants. © 2026 Society of Chemical Industry.
Borg et al. (Sun,) studied this question.