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The Bone & Joint Journal
Vol. 102-B, Issue 10 | Pages 1331 - 1340
3 Oct 2020
Attard V Li CY Self A Mann DA Borthwick LA O’Connor P Deehan DJ Kalson NS

Aims

Stiffness is a common complication after total knee arthroplasty (TKA). Pathogenesis is not understood, treatment options are limited, and diagnosis is challenging. The aim of this study was to investigate if MRI can be used to visualize intra-articular scarring in patients with stiff, painful knee arthroplasties.

Methods

Well-functioning primary TKAs (n = 11), failed non-fibrotic TKAs (n = 5), and patients with a clinical diagnosis of fibrosis1 (n = 8) underwent an MRI scan with advanced metal suppression (Slice Encoding for Metal Artefact Correction, SEMAC) with gadolinium contrast. Fibrotic tissue (low intensity on T1 and T2, low-moderate post-contrast enhancement) was quantified (presence and tissue thickness) in six compartments: supra/infrapatella, medial/lateral gutters, and posterior medial/lateral.


The Bone & Joint Journal
Vol. 98-B, Issue 10_Supple_B | Pages 16 - 21
1 Oct 2016
Jones GG Kotti M Wiik AV Collins R Brevadt MJ Strachan RK Cobb JP

Aims

To compare the gait of unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) patients with healthy controls, using a machine-learning approach.

Patients and Methods

145 participants (121 healthy controls, 12 patients with cruciate-retaining TKA, and 12 with mobile-bearing medial UKA) were recruited. The TKA and UKA patients were a minimum of 12 months post-operative, and matched for pattern and severity of arthrosis, age, and body mass index.

Participants walked on an instrumented treadmill until their maximum walking speed was reached. Temporospatial gait parameters, and vertical ground reaction force data, were captured at each speed. Oxford knee scores (OKS) were also collected. An ensemble of trees algorithm was used to analyse the data: 27 gait variables were used to train classification trees for each speed, with a binary output prediction of whether these variables were derived from a UKA or TKA patient. Healthy control gait data was then tested by the decision trees at each speed and a final classification (UKA or TKA) reached for each subject in a majority voting manner over all gait cycles and speeds. Top walking speed was also recorded.