Clinical outcome modelling of rapid dynamics in acute stroke with joint-detail, continuous, remote, body motion analysis

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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.Here we propose to use MoCat, our prototype motion categorisation system, to study the rapid dynamics of acute stroke, seamlessly embedded in the clinical stream. It can robustly determine the skeletal frame despite variations in patient size, clothing or presence of bed covering, and continuously monitor body motion at a short distance from the patient without need for extraneous wires or cables. Consequently, each bed on the hyperacute stroke unit will have its own dedicated device installed that will not interfere with the day-to-day activities on the ward. 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.Past work has shown the pattern of injury to the brain from a stroke impacts on the future outcome of the patient. We will therefore create models that combine our new body motion measures, with brain scans obtained routinely as part of the hyper acute stroke pathway. In this way we not only aim to improve the accuracy of our predictions, but also examine the relationship between the pattern of stroke brain injury and motor recovery.This project is aligned with a large-scale, Wellcome-funded, collaborative programme of translational research with the aim of creating a foundational framework for complex modelling of clinical and imaging data to predict outcomes in acute stroke.