Grant: Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Simon Fraser University
$495,000.00
AI-Driven Decision Support System for Clinical Electroencephalography (EEG) and Medical Records
FY 2021-22 · 2022-03-30
Summary
Artificial Intelligence (AI) is quickly penetrating healthcare, but the routine neurological assessment of patients at the primary point of care is still missing AI-enhanced neurologist-like approaches to deal with the complexity of all its data. Developing such AI tools is the focus of the Project, which seeks to address the primary question of whether a novel state-of-the-art machine learning approach, applied to routine clinical EEGs recorded in hospital, can support a more accurate diagnosis with higher efficiency for all categories of patients requiring neurological assessment at the primary point of care. The team will develop a prototype system to support experts in reviewing EEG data. The novel system will utilize state-of-the-art AI methods to identify key diagnostic and outcome-predictive features in the EEG data. The aim is to design AI systems to enhance workflow in data-intensive and time-sensitive settings.
Department
Program
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Recipient
Location
Burnaby|Burnaby, British Columbia, Canada
Recipient Type
Academia
Business Number
118520725
Type
Grant
Source
Proactive Disclosure - Grants and Contributions
As published
Agreement End Date
2025-03-31
Agreement Number
988617
Agreement Start Date
2022-03-30
Agreement Title
AI-Driven Decision Support System for Clinical Electroencephalography (EEG) and Medical Records
Agreement Title (French)
Système commandé par intelligence artificielle pour faciliter les décisions cliniques à partir des électroencéphalogrammes (EEG) et du dossier médical
Agreement Type
Grant
Agreement Value
$495,000.00
Amendment Number
0
Description
Artificial Intelligence (AI) is quickly penetrating healthcare, but the routine neurological assessment of patients at the primary point of care is still missing AI-enhanced neurologist-like approaches to deal with the complexity of all its data. Developing such AI tools is the focus of the Project, which seeks to address the primary question of whether a novel state-of-the-art machine learning approach, applied to routine clinical EEGs recorded in hospital, can support a more accurate diagnosis with higher efficiency for all categories of patients requiring neurological assessment at the primary point of care. The team will develop a prototype system to support experts in reviewing EEG data. The novel system will utilize state-of-the-art AI methods to identify key diagnostic and outcome-predictive features in the EEG data. The aim is to design AI systems to enhance workflow in data-intensive and time-sensitive settings.
Description (French)
L’intelligence artificielle (IA) a vite trouvé sa place en médecine. Pourtant, on manque toujours d’approches IA qui reproduisent la démarche du neurologue pour traiter les données complexes pour évaluer l’état neurologique du patient au point d’intervention primaire. Mettre au point de tels outils est le but principal du projet, dans le cadre duquel on s’efforcera de répondre à une question primordiale : l’application d’une nouvelle méthode d’apprentissage automatique très pointue aux EEG cliniques pris couramment à l’hôpital aboutirait-elle à un diagnostic précis pour les patients de toute sorte qui nécessitent une évaluation neurologique au point d’intervention primaire? L’équipe créera le prototype d’un système qui aidera les spécialistes à analyser les EEG. Ce système novateur recourra aux plus récentes méthodes d’IA pour déterminer les principaux diagnostics et les valeurs de l’EEG pour prévoir l’issue du traitement, l’objectif étant d’obtenir un système IA qui fera gagner du temps dans les situations où il est compté et où il faut traiter une masse importante de données.
Expected Results
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Expected Results (French)
À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides.
Federal Riding
Burnaby North–Seymour
Federal Riding (French)
Burnaby-Nord–Seymour
Federal Riding Number
59002
NAICS Code
541710
Organization Code
nrc-cnrc
Organization
National Research Council Canada | Conseil national de recherches Canada
Program Name
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Name (French)
Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D
Program Purpose
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
Program Purpose (French)
Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires.
Recipient Business Number
118520725
Recipient City
Burnaby|Burnaby
Recipient Country
CA
Recipient Legal Name
Simon Fraser University
Recipient Postal Code
V5A 1S6
Recipient Province or Territory
BC
Recipient Type
S
Reference Number
172-2021-2022-Q4-988617