Smart Patches for Early Detection of Knee Osteoarthritis
Lead Research Organisation:
Cardiff University
Department Name: Sch of Engineering
Abstract
EPSRC Portfolio Research Areas: Assistive technology, rehabilitation and musculoskeletal biomechanics (maintain), Clinical technologies (excluding imaging) (maintain)
Description:
8.75 million people in the UK have sought treatment for osteoarthritis (OA) and the condition costs the UK economy £10B/year (Arthritis Research UK). An early and reliable detection system for OA can provide savings on expensive diagnostic tools (x-ray, MRI...), and greatly improve the patients' lifestyle and health by allowing early detection and intervention.
When joints develop OA, they can make audible grating or clicking noises during movement, indicating friction between bone and cartilage (Crepitus). This occurs during the later stages of the disease, when it is too late to intervene to arrest further disease development. In OA patients, being able to detect smaller cartilage lesions or instabilities, would allow for a much earlier stage detection (for example, in a GP surgery or even in self-monitoring, linked to a mobile or tablet app), without the need for expensive imaging costs and expertise. This would allow earlier intervention when the inflammatory process and the degenerative process have not fully developed yet.
The technology to detect these sounds, especially in the non-audible range, is established in materials testing under the name of "Acoustic Emission" (AE). AE biomedical research applications exist, but OA studies are limited and never linked sounds with detailed biomechanical and imaging assessment.
The main aim of the studentship is to develop and validate a low profile smart patch, and assess it on healthy and OA individuals. Alongside biomechanics, x-ray, MRI and pain analysis, the project aims to match the accuracy of these techniques in evaluating the stage of OA by using established metrics such as the Kellgren and Lawrence (KL) grade.
It is expected that the combination of traditional, gold standard techniques with our innovative sensor based approach will open further areas of research, such as self-monitoring devices and haptic devices for self-administered or remotely monitored rehabilitation.
Description:
8.75 million people in the UK have sought treatment for osteoarthritis (OA) and the condition costs the UK economy £10B/year (Arthritis Research UK). An early and reliable detection system for OA can provide savings on expensive diagnostic tools (x-ray, MRI...), and greatly improve the patients' lifestyle and health by allowing early detection and intervention.
When joints develop OA, they can make audible grating or clicking noises during movement, indicating friction between bone and cartilage (Crepitus). This occurs during the later stages of the disease, when it is too late to intervene to arrest further disease development. In OA patients, being able to detect smaller cartilage lesions or instabilities, would allow for a much earlier stage detection (for example, in a GP surgery or even in self-monitoring, linked to a mobile or tablet app), without the need for expensive imaging costs and expertise. This would allow earlier intervention when the inflammatory process and the degenerative process have not fully developed yet.
The technology to detect these sounds, especially in the non-audible range, is established in materials testing under the name of "Acoustic Emission" (AE). AE biomedical research applications exist, but OA studies are limited and never linked sounds with detailed biomechanical and imaging assessment.
The main aim of the studentship is to develop and validate a low profile smart patch, and assess it on healthy and OA individuals. Alongside biomechanics, x-ray, MRI and pain analysis, the project aims to match the accuracy of these techniques in evaluating the stage of OA by using established metrics such as the Kellgren and Lawrence (KL) grade.
It is expected that the combination of traditional, gold standard techniques with our innovative sensor based approach will open further areas of research, such as self-monitoring devices and haptic devices for self-administered or remotely monitored rehabilitation.
