This work package is an exemplar project using AI to improve acute stroke workflow. The project is developing technology aimed at optimising clinical workflow in the setting of acute ischaemic stroke for patients being considered for intravenous thrombolysis (or other perfusion therapies). In addition to the specification, research, development, testing & documentation of an Acute Stroke Clinical Cockpit, this project is using Canons’ Safe Haven Artificial Intelligence Platform (SHAIP) systems installed at the Glasgow Queen Elizabeth University Hospital and Aberdeen Royal Infirmary to develop machine learning algorithms aimed at streamlining workflow, identifying key treatment (contra)indications, predicting outcomes, calculating risk/benefit scores and generally supporting clinical decision making in this area.
Aims & Objectives
The key objectives of this project are:
- The specification, research, development, testing & documentation of software for the Acute Stroke Clinical Cockpit.
- Optimization of clinical diagnostic & treatment workflows for acute ischemic stroke patients being considered for intravenous thrombolysis or or other perfusion therapies.
- To support & accelerate clinical decision making by deployment of an AI-enabled Acute Stroke Clinical Cockpit at Glasgow’s Queen Elizabeth University Hospital.
- To support the evaluation of federated AI learning in Aberdeen.
Canon are collaborating with SME DeepCognito to optimize diagnostic and clinical decision workflow in the setting of acute ischaemic stroke patients being considered for intravenous thrombolysis or other perfusion therapies.
Work Package Leader: Canon Medical Research Europe
Contributing Partners: DeepCognito Ltd. University of Glasgow, University of Aberdeen, NHS Grampian, NHS Greater Glasgow and Clyde