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Background <p>In order to optimise clinical outcomes after primary total knee arthroplasty (TKA), research has refocused on the knee joint’s biomechanical characteristics. Beyond implant design and alignment philosophy, the restoration of natural joint kinematics, functional range of motion, and stability critically depends on patient-specific anatomical conditions.</p> Biomechanics <p>Instrumented TKA systems have demonstrated the significance of coronal alignment and mediolateral load distribution. Furthermore, patellofemoral joint alignment has gained attention as a&#xa0;determinant of postoperative success. While static radiographic assessments remain the gold standard, they can be meaningfully complemented by instrumented gait analysis to capture dynamic leg alignment and quantify the influence of the external knee adduction moment. Furthermore, computational simulations facilitate a&#xa0;more precise analysis of implant-specific loading conditions and kinematic behaviour.</p> New methods <p>In combination with experimental approaches, such as in vitro kinematic testing, these tools facilitate a&#xa0;detailed evaluation of complex movement and load scenarios, thereby supporting the development of personalised therapeutic and rehabilitative strategies. Furthermore, the development of novel classifications of kinematic phenotypes holds great potential for the systematic categorisation of patients and the personalisation of interventions, with the aim of enhancing functional outcomes and satisfaction. The use of subject-specific musculoskeletal models and finite element analysis (FEA) permits the simulation of joint mechanics under individual anatomical constraints, thus contributing to the optimisation of implant positioning and the reduction of biomechanical load. In the future, the integration of artificial intelligence and machine learning into preoperative planning is expected to refine patient-specific treatment algorithms.</p> Prospect <p>The clinical translation of these biomechanical insights will ultimately require validation in larger patient cohorts in order to substantiate their efficacy and long-term benefit.</p>

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Update 2025: Biomechanik und Kinematik nach Knie-TEP

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Markus Heinecke,
  • Leandra Bauer,
  • Benjamin Jacob,
  • Julia Kirschberg,
  • Arnd Steinbrück,
  • Georg Matziolis,
  • Matthias Woiczinski

摘要

Background

In order to optimise clinical outcomes after primary total knee arthroplasty (TKA), research has refocused on the knee joint’s biomechanical characteristics. Beyond implant design and alignment philosophy, the restoration of natural joint kinematics, functional range of motion, and stability critically depends on patient-specific anatomical conditions.

Biomechanics

Instrumented TKA systems have demonstrated the significance of coronal alignment and mediolateral load distribution. Furthermore, patellofemoral joint alignment has gained attention as a determinant of postoperative success. While static radiographic assessments remain the gold standard, they can be meaningfully complemented by instrumented gait analysis to capture dynamic leg alignment and quantify the influence of the external knee adduction moment. Furthermore, computational simulations facilitate a more precise analysis of implant-specific loading conditions and kinematic behaviour.

New methods

In combination with experimental approaches, such as in vitro kinematic testing, these tools facilitate a detailed evaluation of complex movement and load scenarios, thereby supporting the development of personalised therapeutic and rehabilitative strategies. Furthermore, the development of novel classifications of kinematic phenotypes holds great potential for the systematic categorisation of patients and the personalisation of interventions, with the aim of enhancing functional outcomes and satisfaction. The use of subject-specific musculoskeletal models and finite element analysis (FEA) permits the simulation of joint mechanics under individual anatomical constraints, thus contributing to the optimisation of implant positioning and the reduction of biomechanical load. In the future, the integration of artificial intelligence and machine learning into preoperative planning is expected to refine patient-specific treatment algorithms.

Prospect

The clinical translation of these biomechanical insights will ultimately require validation in larger patient cohorts in order to substantiate their efficacy and long-term benefit.