COMPutational Assessment of Stroke Survivors Prototype (COMPASS-proto)

Project Description

Background: Stroke affects 152,000 UK citizens every year. Over 50% of stroke survivors have cognitive impairment. Currently 850,000 people live with dementia in the UK and stroke is one of the biggest risk factors. National guidelines promote early cognitive testing on all people who have had a stroke. However, current pen-and-paper based tests are not always appropriate for stroke survivors who often have motor, visual or language difficulties. Currently, assessments typically take place in hospital settings, are costly and often inconvenient for patients. In addition, longitudinal follow-up is required to detect emerging cognitive impairment.
Aims & Objectives: This project aims to create an easy-to-use cognitive assessment tool specifically designed for the needs of stroke survivors. It will be based on our stratification tool COCOA (COmputational COgnitive Assessment), developed for detecting early signs of dementia. The tool uses automatic analysis of conversations that patients have with an on-screen digital doctor. The patients’ speech is and analysed for signs of cognitive decline using speech recognition and machine learning classification. We have demonstrated high stratification accuracy in distinguishing between healthy controls, people with mild cognitive impairment (MCI) and people with Alzheimer’s disease. Feedback suggests that patients find the system straightforward and pleasant to use. Methodology: This project will adapt our digital doctor’s questions to target vascular cognitive impairment, especially executive dysfunction, neglect and dysphasia. We will create an online version to enable home-based testing.

People & Partners

In collaborations with Dr Kirsty Harkness (Stroke Physician and Cognitive Neurologist, Royal Hallamshire Hospital); Prof Annalena Venneri (Neuropsychology, Royal Hallamshire Hospital); Prof Markus Reuber, (Dept of Neurology, University of Sheffield); Dr Traci Walker (Clinical Linguist, Dept of Human Communication Sciences, University of Sheffield);

Funders

This project has received funding by the FAST Healthcare NetworksPlus Project - £49,662.