Grant: Canada Research Chairs
Toronto Metropolitan University
$120,000.00
AI for Medical Imaging
EF 2024-25
Résumé
Neurological disorders such as dementia, breast and colon cancer, and renal diseases place a heavy economic and health burden on Canadian society. Medical imaging is a critical way to diagnose disease, monitor disease progression, and to plan treatment. Manual analysis of medical images is both subjective and time consuming, leading to delays in delivering treatment, worse patient outcomes and reduced quality of care (QoC).
As medical imaging data is acquired digitally, novel artificial intelligence (AI) algorithms can be leveraged to improve Turn-Around-Times (TATs), healthcare access and QoC. Computer-generated biomarkers from medical images can improve diagnostic accuracy, TAT and increase access to radiological and pathological expertise. While biomarkers should be comparable across different sources and scanning methods, variances in the way medical data was originally acquired in the hospital make it difficult to accurately compare, analyze data and extract relevant results.
Building on her expertise in AI tool development, medical imaging and biomedical engineering, Dr. April Khademi’s research program will overcome these challenges through computational resources and novel AI algorithms for multicentre Magnetic Resonance Images (MRI) and digital pathology images. Her research program will focus on (1) digital radiology, and in particular, neurological MRI, with the goal of improving glioma, dementia and vascular disease progression monitoring, determining optimal intervention times and learning more about disease; and (2) digital pathology focused on breast, renal and colorectal polyps due to the prevalence of disease in these organs, and the need for high volume interpretation. Her research will be used to augment radiology and pathology workflows to enhance diagnostic accuracy and medical imaging TATs for common cancers and neurological illnesses, making a significant contribution to Canadians’ health.
Programme
Canada Research Chairs
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
CRC-2022-00076
Name nom
Khademi, April
Department département
Department of Electrical, Computer and Biomedical Engineering - Toronto Metropolitan University
Organization id
43
Institution établissement
Toronto Metropolitan University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
120000
Program id
CRC
Program name en
Canada Research Chairs
Program name fr
Chaires de recherche du Canada
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
9998
Committee name en
Canada Research Chair Selection Committee
Committee name fr
Comité de sélection des Chaires de recherche du Canada
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Non disponible
Research subject en
Not available
Research subject fr
Non disponible
Field of research list codes
RDF2070199
Field of research list names en
Medical and biomedical engineering, n.e.c.
Field of research list names fr
Génie médical et biomédical, n.c.a.
Socioeconomic objective list codes
RDS10799
Socioeconomic objective list names en
Health, n.e.c.
Socioeconomic objective list names fr
Santé, n.c.a.
Application title
AI for Medical Imaging
Keywords
medical imaging; machine learning; image analysis; AI; neuroimaging; MRI; digital pathology; cancer; dementia; research and development
Application summary
Neurological disorders such as dementia, breast and colon cancer, and renal diseases place a heavy economic and health burden on Canadian society. Medical imaging is a critical way to diagnose disease, monitor disease progression, and to plan treatment. Manual analysis of medical images is both subjective and time consuming, leading to delays in delivering treatment, worse patient outcomes and reduced quality of care (QoC).
As medical imaging data is acquired digitally, novel artificial intelligence (AI) algorithms can be leveraged to improve Turn-Around-Times (TATs), healthcare access and QoC. Computer-generated biomarkers from medical images can improve diagnostic accuracy, TAT and increase access to radiological and pathological expertise. While biomarkers should be comparable across different sources and scanning methods, variances in the way medical data was originally acquired in the hospital make it difficult to accurately compare, analyze data and extract relevant results.
Building on her expertise in AI tool development, medical imaging and biomedical engineering, Dr. April Khademi’s research program will overcome these challenges through computational resources and novel AI algorithms for multicentre Magnetic Resonance Images (MRI) and digital pathology images. Her research program will focus on (1) digital radiology, and in particular, neurological MRI, with the goal of improving glioma, dementia and vascular disease progression monitoring, determining optimal intervention times and learning more about disease; and (2) digital pathology focused on breast, renal and colorectal polyps due to the prevalence of disease in these organs, and the need for high volume interpretation. Her research will be used to augment radiology and pathology workflows to enhance diagnostic accuracy and medical imaging TATs for common cancers and neurological illnesses, making a significant contribution to Canadians’ health.