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Orthopaedic Proceedings
Vol. 103-B, Issue SUPP_16 | Pages 53 - 53
1 Dec 2021
De Vecchis M Naili JE Wilson C Whatling GM Holt CA
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Abstract

Objectives

Exploring the relationship of gait function pre and post total knee replacement (TKR) in two groups of patients.

Methods

Three-dimensional gait analysis was performed at Cardiff University, UK, and Karolinska University Hospital, Sweden, on 29 and 25 non-pathological (NP) volunteers, and 39 and 28 patients with end-stage knee osteoarthritis (OA), respectively. Patients were assessed pre and one-year post-TKR. Data reduction was performed via Principal Component (PC) analysis on twenty-four kinematic and kinetic waveforms in both NP and pre/post-TKR. Cardiff's and Karolinska's cohorts were analysed separately. The Cardiff Classifier, a classification system based on the Dempster-Shafer theory, was trained with the first 3 PCs of each variable for each cohort. The Classifier classifies each participant by assigning them a belief in NP, belief in OA (BOA) and belief in uncertainty, based on their biomechanical features. The correlation between patient's BOA values (range: 0–1, 0 indicates null BOA and 1 high BOA) pre and post-TKR was tested through Spearman's correlation coefficient in each cohort. The related-samples Wilcoxon signed-rank test (α=0.05) determined the significant changes in BOA in each cohort of patients. The Mann-Whitney U test (α=0.05) was run to explore differences between the patients’ cohorts.


Orthopaedic Proceedings
Vol. 103-B, Issue SUPP_2 | Pages 30 - 30
1 Mar 2021
De Vecchis M Biggs PR Wilson C Whatling GM Holt CA
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Abstract

Objectives

Exploring the association of objective lower limb function pre and post total knee replacement (TKR).

Methods

3D gait analysis was performed on 28 non-pathological participants (NP) and 40 patients with advanced knee osteoarthritis (OA) before and approximately one year after TKR. For NP and OA patients pre/post-TKR, 12 waveforms on kinetic and kinematic variables of the operative side were chosen to perform data reduction through Principal Component (PC) Analysis. The Cardiff Classifier, a classification system based on Dempster-Shafer theory, was trained with the first 3 PCs of each variable. The 18 highest-ranking PCs classifying the biomechanical features of each participant as Belief in Healthy, Belief in OA (BOA) or Belief in Uncertainty were used to quantify biomechanical changes pre- to post-TKR. The correlation between patients’ BOA values (range: 0 to 1, 0 indicates null BOA and 1 high BOA) pre- and post-TKR was tested through Spearman's correlation coefficient. Wilcoxon matched-pair test (α<0.05) determined the significance of the change in BOA.


Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_XVIII | Pages 44 - 44
1 May 2012
Whatling GM Wilson C Holt CA
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INTRODUCTION

Useful feedback from a Total Knee Replacement (TKR) can be obtained from post-surgery in-vivo assessments. Dynamic Fluoroscopy and 3D model registration using the method of Banks and Hodge (1996) [1] can be used to measure TKR kinematics to within 1° of rotation and 0.5mm of translation, determine tibio-femoral contact locations and centre of rotation. This procedure also provides an accurate way of quantifying natural knee kinematics and involves registering 3D implant or bone models to a series of 2D fluoroscopic images of a dynamic movement.

AIM

The aim of this study was to implement a methodology employing the registration methods of Banks and Hodge (1996) [1] to assess the function of different TKR design types and gain a greater understanding of non-pathological (NP) knee biomechanics.


Orthopaedic Proceedings
Vol. 92-B, Issue SUPP_III | Pages 407 - 407
1 Jul 2010
Whatling GM Larcher M Young P Evans J Jones D Banks SA Fregly BJ Khurana A Kumar A Williams RW Wilson C Holt CA
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Introduction: Inaccuracies in kinematic data recording due to skin movement artefact are inherent with motion analysis. Image registration techniques have been used extensively to measure joint kinematics more accurately. The aim of this study was to assess the feasibility of using MRI for creating 3D models and to quantify errors in data collection methods by comparing kinematics computed from motion analysis and image registration.

Methodology : 5 healthy and 5 TKR knees were examined for a step up/down task using dynamic fluoroscopy and motion capture. MRI scans of the knee, femur and tibia were performed on the healthy subjects and were subsequently segmented using ScanIP(Simpleware) to produce 3D bone models. Registration of the models produced from fine and coarse scan data was used to produce bony axes for the femoral and tibial models. Tibial and femoral component CAD models were obtained for the TKR patients. The 3D knee solid models and the TKR CAD models were then registered to a series of frames from the 2D fluoroscopic image data (Figure 1) obtained for the 10 subjects, using KneeTrack(S. Banks, Florida) to produce kinematic waveforms. The same subjects were also recorded whilst performing the same action, using a Qualisys (Sweden) motion capture system with a pointer and marker cluster-based technique developed to quantify the knee kinematics.

Results: The motion analysis method measured significantly larger frontal and transverse knee rotations and significantly larger translations than the image registration method.

Conclusion: The study demonstrated that MRI, rather than CT scan, can be used as a non-invasive tool for developing segmented 3D bone models, thus avoiding highly invasive CT scanning on healthy volunteers. It describes an application of combining fine and coarse scan models to establish anatomical or mechanical axes within the bones for use with kinematic modeling software. It also demonstrates a method to investigate errors associated with measuring knee kinematics.