Clinical outcome modelling of rapid dynamics in acute stroke
Research type
Research Study
Full title
Clinical outcome modelling of rapid dynamics in acute stroke with joint-detail, remote, body motion analysis
IRAS ID
268201
Contact name
Rahman Ahmed
Contact email
Sponsor organisation
King's College Hospital NHS Foundation Trust
Duration of Study in the UK
3 years, 6 months, 7 days
Research summary
Stroke - still the second commonest cause of death and principal cause of adult neurological disability in the Western World - is characterised by rapid changes over time and marked variability in outcomes. A patient may improve or deteriorate over minutes, and the resultant disability may range from an obvious complete paralysis to subtle, task-dependent incoordination of a single limb.
Unlike many other neurological disorders, stroke can be exquisitely sensitive to prompt and intelligently tailored treatment, rewarding innovation in the delivery of care with real-world, tangible impact on patient outcomes. Optimal treatment therefore requires both detailed characterisation of the patient's clinical picture and its pattern of change over time.
Arguably the most important aspect of the patient's clinical picture -- body movement -- remains remarkably poorly documented: quantified only subjectively and at infrequent intervals in the patient's clinical evolution. The combination of artificial intelligence with high-performance computing now enables automatic extraction of a patient's skeletal frame resolved down to major joints, like that of a stick-man, to be delivered simply, safely, and inexpensively, without the use of cumbersome body worn markers. Central to this technology is patient privacy, with the skeletal frame extracted in real time, ensuring no video data, from which patients can be identified, to be stored or transmitted by the device.
Our prototype motion categorisation system -- MoCat -- will be used to study the rapid dynamics of acute stroke, seamlessly embedded in the clinical stream. By quantifying the change in motor deficit over time we shall examine the relationship between these trajectories with clinical outcomes and develop predictive models that can support clinical management and optimise service delivery.REC name
London - Bromley Research Ethics Committee
REC reference
20/LO/0159
Date of REC Opinion
27 Apr 2020
REC opinion
Further Information Favourable Opinion