Resumen: Total knee arthroplasty (TKA) is a common procedure that has become the standard of treatment for severe cases of knee osteoarthritis. Biomechanics and quality of movement similar to healthy were found to improve patient-reported outcomes.
In this study, an evaluated musculoskeletal model predicted ligament, contact and muscle forces together with secondary tibiofemoral kinematics. An artificial neural network applied to the musculoskeletal model searched for the optimal implant position in a given range that will minimize the root-mean-square-error (RMSE) between post- TKA and native experimental tibiofemoral kinematics during a squat.
We found that, using a cruciate-retaining implant, native kinematics could be accurately reproduced (average RMSE 1.47 mm (± 0.89 mm) for translations and 2.89° (± 2.83°) for rotations between native and optimal TKA alignment). The required implant positions changes maximally 2.96 mm and 2.40o. This suggests that when using pre- operative planning, off-the-shelf CR implants allow for reproducing native knee kinematics post-operatively. Idioma: Inglés DOI: 10.29007/lj2j Año: 2020 Publicado en: EPiC series in health sciences 4 (2020), 61-64 ISSN: 2398-5305 Financiación: info:eu-repo/grantAgreement/EC/H2020/722535/EU/Predictive models and simulations in bone regeneration: a multiscale patient-specific approach/CuraBone Tipo y forma: Artículo (Versión definitiva) Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)