Professor Alison McGregor
Professor of Musculoskeletal Biodynamics
Department of Surgery & Cancer, Imperial College London, UK

Date: Thursday, 31-May-2018
Time: 4:00 p.m.
Venue: A2-08, Mrs. Chen Yang Foo Oi Telemedicine Centre
2/F, William MW Mong Block, Faculty of Medicine Building
21 Sassoon Road, Pokfulam, Hong Kong

Summary: 
According to Arthritis Research UK 4.7 million people in the UK have knee osteoarthritis (OA) and this figure is estimated to rise to 6.5 million by 2020. Not surprisingly it is a major source of pain and disability and one that creates a substantial socioeconomic burden. Osteoarthritis is a chronic disease state characterized by pain, disability and radiographic changes. Agreed markers that facilitate a clinical diagnosis of osteoarthritis however represent the late stage of a degenerative process. Chu et al (2012) have proposed that greater focus should be on a “pre-osteoarthritic” state as this will facilitate early diagnosis and as such open opportunities for early treatment and palliation of late disease, and ideally prevention.

Currently a range of approaches are used to formalise a diagnosis of OA, recent guidelines suggest that a clinical diagnosis can be achieved based on symptoms and signs. Whilst others indicate the need for radiographic findings utilising tools such as the Kellegren and Lawrence scale, despite poor correlation of radiographic findings to clinical symptoms and signs. Whilst novel MRI techniques are being developed to assess cartilage and bone morphology, such approaches are unsuitable for large scale studies or clinical application due to associated high costs. There is also on-going research to identify OA biomarkers but as yet the clinical application as stated by Glyn-Jones et al (2015) it “is a fairly distant prospect and many obstacles remain”. Therefore if we are to develop early interventions for the treatment and prevention of OA we need to explore novel approaches to identifying clinically translatable markers of early OA.

In this talk, we will explore the use of advanced mathematical and computational approaches (machine learning techniques) applied to biomechanical parameters of gait as early markers of OA and the use of wearable technology approaches to translate such findings to clinical care.

ALL ARE WELCOME