Evidence map›Paper›PMID 42201402›Full record

ArticleArchives of orthopaedic and trauma surgery2026

Changes in kinematic behavior and collateral ligament strain after medially stabilized TKA using a novel intraoperative navigation platform: a cadaveric study.

Arne Van de Vyver, Orçun Taylan, Geert Peersman, Margot Demeulenaere, Maciej Bialy, Thomas Lauwagie, Lennart Scheys

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Article in Archives of orthopaedic and trauma surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Arne Van de VyverDepartment of Orthopedic surgery, ZAS Cadix, Antwerp, Belgium. arne.vandevyver@zas.be.
Orçun TaylanInstitute of Orthopedic Research and Training, Leuven, Belgium.
Geert PeersmanDepartment of Orthopedic surgery, ZAS Cadix, Antwerp, Belgium.
Margot DemeulenaereDepartment of Orthopedic surgery, ZAS Cadix, Antwerp, Belgium.
Maciej BialyInstitute of Orthopedic Research and Training, Leuven, Belgium.
Thomas LauwagieUniversitair Ziekenhuis Leuven, Leuven, Belgium.
Lennart ScheysInstitute of Orthopedic Research and Training, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to analyze to what extent a novel intraoperative navigation platform (Next-AR, Medacta) for total knee arthroplasty (TKA) allows to restore the native knee joint kinematics and strains in the medial collateral ligament (MCL) and lateral collateral ligament (LCL) throughout a squatting motion. MATERIALS AND

methodsComputed tomography (CT) scans of 6 native cadaver legs were used to design patient-specific guides. Bony landmarks and virtual single-line collateral ligaments were identified to acquire real-time intraoperative feedback on bone resection, implant alignment, tibiofemoral kinematics, and collateral ligament elongations using the Next-AR system. The specimens were subjected to squatting (35°-100°) motion using a physiological ex vivo knee simulator while maintaining a constant vertical ankle load of 110 N through active quadriceps and bilateral hamstring controls. Subsequently, each knee underwent a medially-stabilized TKA (Medacta) with mechanical alignment technique and was retested under the same conditions as in their native state. The tibiofemoral and patellofemoral kinematics, along with collateral ligament strains, were computed from 3D marker trajectories using a six-camera optical system (Vicon). MCL and LCL insertions-ant, mid, and post bundles-were identified in relation to bone-pin markers using a wand.

resultsBoth native and post-operative conditions exhibited similar tibial valgus orientation (Root Mean Square Error (RMSE = 1.7°), patellar flexion (RMSE = 1.2°), abduction (RMSE = 0.5°), and rotation (RMSE = 0.4°) during squatting (p > 0.13). However, a significant difference was observed in tibial internal rotation between 35° and 62° (p < 0.048, RMSE = 3.2°). MCL strains in anterior (RMSE = 1.5%), middle (RMSE = 0.7%), and posterior (RMSE = 0.8%) were closely matching in both conditions, showing no statistical differences (p > 0.05). Contrary, LCL strain in all bundles (RMSE < 4.6%) differed significantly from mid to deep flexion in both conditions (p < 0.048).

conclusionThe novel intraoperative Next-AR system not only targets planned knee alignment but also aids in restoring native knee kinematics and elongation of the collateral ligaments through real-time feedback.

Indexed as

Arthroplasty, Replacement, KneeCollateral LigamentsKnee JointMedial Collateral Ligament, KneeSurgery, Computer-AssistedAgedBiomechanical PhenomenaCadaverFemaleHumansMaleRange of Motion, ArticularTibiofemoral JointTomography, X-Ray ComputedCollateral ligament kinematicsComputer-assisted surgeryKnee arthroplastyNavigation

Identifiers

PMID42201402
PMCPMC13216290

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.