000097323 001__ 97323
000097323 005__ 20230622083321.0
000097323 0247_ $$2doi$$a10.29007/lj2j
000097323 0248_ $$2sideral$$a121379
000097323 037__ $$aART-2020-121379
000097323 041__ $$aeng
000097323 100__ $$aDejtiar, David Leandro
000097323 245__ $$aStandard Cruciate-Retaining Total Knee Arthroplasty Implants can Reproduce Native Kinematics
000097323 260__ $$c2020
000097323 5060_ $$aAccess copy available to the general public$$fUnrestricted
000097323 5203_ $$aTotal 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.
000097323 536__ $$9info:eu-repo/grantAgreement/EC/H2020/722535/EU/Predictive models and simulations in bone regeneration: a multiscale patient-specific approach/CuraBone$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 722535-CuraBone
000097323 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000097323 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000097323 700__ $$aBartsoen, Laura
000097323 700__ $$aWesseling, Mariska
000097323 700__ $$aWirix-Speetjens, Roel
000097323 700__ $$aVander Sloten, Jos
000097323 700__ $$0(orcid)0000-0002-2901-4188$$aPerez, Maria Angeles$$uUniversidad de Zaragoza
000097323 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000097323 773__ $$g4 (2020), 61-64$$pEPiC ser. health sci.$$tEPiC series in health sciences$$x2398-5305
000097323 8564_ $$s437740$$uhttps://zaguan.unizar.es/record/97323/files/texto_completo.pdf$$yVersión publicada
000097323 8564_ $$s315810$$uhttps://zaguan.unizar.es/record/97323/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000097323 909CO $$ooai:zaguan.unizar.es:97323$$particulos$$pdriver
000097323 951__ $$a2023-06-21-15:03:05
000097323 980__ $$aARTICLE