Gait analysis systems have enjoyed increasing usage and have been validated to provide highly accurate assessments for range of motion. Size, cost, need for marker placement and need for complex data processing have remained limiting factors in uptake outside of what remains predominantly large research institutions. Progress and advances in deep neural networks, trained on millions of clinically labelled datasets, have allowed the development of a computer vision system which enables assessment using a handheld smartphone with no markers and accurate range of motion for knee during flexion and extension. This allows clinicians and therapists to objectively track progress without the need for complex and expensive equipment or time-consuming analysis, which was concluded to be lacking during a recent systematic review of existing applications. A smartphone based computer vision system was assessed for accuracy with a gold standard comparison using a validated ‘traditional’ infra-red motion capture system which had a defined calibrated accuracy of 0.1degrees. A total of 22 subjects were assessed simultaneously using both the computer vision smartphone application and the standard motion capture system. Assessment of the handheld system was made by comparison to the motion capture system for knee flexion and extension angles through a range of motion with a simulated fixed-flexion deformity which prevented full extension to assess the accuracy of the system, repeating movements ten times. The peak extension angles and also numerous discrete angle measurements were compared between the two systems. Repeatability was assessed by comparing several sequential cycles of flexion/extension and comparison of the maximum range of motion in normal knees and in those with a simulated fixed-flexion deformity. In addition, discrete angles were also measured on both legs of three cadavers with both skin and then bone implanted fiducial markers for ground truth reliability accounting for skin movement. Data was processed quickly through an automated secure cloud system.Introduction
Method
Unicompartmental Knee Replacement Arthroplasty (UKA) is a treatment option for early knee OA that appears under-utilised, partly because of a lack of clear guidance on how to best restore lasting knee function using such devices. Computational tools can help consider inherent uncertainty in patient anatomy, implant positioning and loading when predicting the performance of any implant. In the present research an approach for creating patient-specific finite element models (FEM) incorporating joint and muscle loads was developed to assess the response of the underlying bone to UKA implantation. As a basis for future uncertainty modelling of UKA performance, the geometriesof 173 lower limbs weregenerated from clinical CT scans. These were segmented (ScanIP, Simpleware Ltd, UK) to reconstruct the 3D surfaces of the femur, tibia, patella and fibula. The appropriate UKA prosthesis (DePuy, U.S.) size was automatically selected according to tibial plateau size and virtually positioned (Figure 1). Boolean operations and mesh generation were accomplished with ScanIP. A patient-specific musculoskeletal model was generated in open-source software OpenSim (Delp et al. 2007) based on the Gait2392 model. The model was scaled to a specific size and muscle insertion points were modified to corresponding points on lower limb of patient. Hip joint load, muscle forces and lower limb posture during gait cycle were calculated from the musculoskeletal model. The FE meshes of lower limb bones were transformed to the corresponding posture at each time point of a gait cycle and FE analyses were performed (Ansys, Inc. U.S) to evaluate the strain distribution on the tibial plateau in the implanted condition.Introduction
Methods
Increased incidence of obesity and longer life expectancies will place increased demands on load bearing joints. In the present work, a method of pre-clinical evaluation to assess the condition of the joint and potentially inform on cases of joint deterioration, is described. Acoustic emission (AE) is a non-destructive test methodology that has been used extensively in engineering for condition monitoring of machinery and structures. It is a passive technique that uses piezoelectric sensors to detect energy released from internal structural defects as they deform and grow. The technique has been used with some success in the past to identify characteristic signals generated from the knee joint during activities such as standing and sitting, in candidate arthroplasty patients (1,2). In this study, 40 asymptomatic subjects had AE data generated from their knee joints analysed. Subject characteristics such as age, gender, and lifestyle were disclosed and evaluated against the AE data. Each subject was invited to take a seated position and a piezoelectric AE sensor (Pancom P15, 150kHz resonance, 19mm diameter) was attached to the subject's knee using a wax couplant and tape as close to the articulating surface and on a bony prominence to avoid signal attenuation in the soft tissue. Subjects were invited to sit and stand 3 times. AE data were collected and processed using an AMSY5 AE processor (Vallen, Germany). Tests were repeated on a separate occasion and selected subjects were invited to participate on a third occasion. The AE data of particular interest were the peak amplitudes and the frequency power spectrum of the waveform. Post-test inspection of subject characteristics allowed them to be separated into three broad categories: no previous history (group A), some instances of pain in the knee (group B), and those who have had previous minor surgery on the knee (group C). The corresponding AE results were grouped separately. It was found that groups A and B demonstrated similar signal amplitude characteristics while group C produced much higher, significantly different (p<0.05) amplitudes and amplitude distributions. Typical results are shown in figure 1. At present, broad trends could be identified and relationships emerged between the data and subject history (prior surgery, typical daily activity). Further work will continue with asymptomatic subjects and the work will be extended to pre-operative patients to identify whether certain trends are amplified in this population.
Revision knee arthroplasty is increasing and in 2010 constituted 6% of knee replacements done in the UK according to the National Joint Registry1. Infection was the 2nd most common cause accounting for 23% of the revision burden1. Two-stage revisions are considered the gold standard with success rates from 80–100%2. Single-stage revisions are becoming increasingly popular at certain centers with reported benefits of reduced “down-time” for the patient and a decreased financial burden. The senior author (DSB) has been performing single-stage revisions for infections for over 10 years. We were interested in seeing the success rate for this method and possibly identify factors that would portend a poorer result.INTRODUCTION
OBJECTIVES
We performed an advancement and medial transfer of the tibial tuberosity based on Fulkerson's principle to treat intractable anterior knee pain associated with patellofemoral maltracking diagnosed by dynamic MRI. Between January 1998 and July 2000 twenty-two patients had 28 knees operated for anterior knee pain. There were 4 men and 18 women with a mean age of 28 years (range 18-41). Indications for surgery were [a] failure to improve after six months of physiotherapy and [b] patellofemoral maltracking evident in dynamic MRI. Mean follow-up was for 37 months (23 – 42). Knee instability score modified by Fulkerson was employed for objective and subjective assessment. Objectively 22 (79%) knees achieved good to excellent results. Four knees (14%) had fair, and two (7%) had poor results. Excellent and very good results were seen in 20 knees. These patients were a younger age group (mean age 21 years) and had minimal degeneration (grade I-II) of the patellofemoral joints. Two patients achieved good results. One of them had moderate (grade III) and one minimal (II) arthritis. Three knees with fair results had advanced (grade IV or V) and one had moderate (grade III) arthritis. Out of two patients who had a poor result, one had advanced degeneration (grade V) that later required a patellofemoral joint resurfacing. The other was a 24 year old woman with grade II changes. She was treated by the pain therapy team. Anterior displacement of the tuberosity in the presented study was kept to 5 mm to avoid the possible complications of wound break down. The overall length and depth of the osteotomy was also reduced to minimise risk of fracture and commence early mobilisation. Based on our results there is a strong case of justification for Anteromedialisation of tibial tuberosity using a smaller length of osteotomy and lesser degree of anteriorisation in carefully selected patients with Patellofemoral arthralgia associated with maltracking patella.
