Grant: Alliance Grants
Université de Moncton
$50,000.00
Detection of COVID-19 using AI - Deep learning and multiple medical imaging modalities
FY 2020-21
Summary
SARS-CoV2 is a new coronavirus identified as the cause of the 2019 coronavirus disease (COVID-19) which started in Wuhan, China in late 2019 and has spread around the world. This pandemic is impacting the daily lives of everyone around the world. Currently, the most common screening and diagnostic test for COVID-19 is a laboratory test called reverse transcription polymerase chain reaction (RT-PCR). This test uses samples collected from the patient (with a nasopharyngeal and or throat swab). The specificity of this test is considered high. However, its sensitivity can be as low as 60-70%, leading to a significant number of false negatives and increasing the risk of community transmission. Other concerns are the test result long turnaround time and the worldwide shortage of reagents and swabs, which limits the number of tests that can be conducted. Experts say that for social distancing measures to be safely lifted, we will need to run a large number of tests.
Medical imaging modalities have also been used to detect signs of COVID-19. More recently, an international panel of experts evaluated the utility of imaging technologies in the management of COVID-19, especially chest radiography (CXR) and computed tomography (CT). Another interesting modality that shows a lot of promise for fast triage of new infections is chest ultrasound (CUS). Recent research shows that all these imaging modalities can be valuable in the fight against this pandemic and can be used along with RT-PCR laboratory tests. The main objective of this project is to develop an AI-based tool capable of detecting COVID-19 using medical imaging modalities of the chest (radiography (CXR), ultrasonography (CUS) and computerized tomography (ChCT)). In addition, we will develop an explainability algorithm to give a visual feedback showing the location of the signs of the disease. This visual feedback will be very useful in helping the confirmation and the stage of the disease by a health practitioner.
_x000d_Le virus SRAS-CoV2 responsable de la maladie à coronavirus COVID-19 a débuté à Wuhan, en Chine, à la fin de 2019 et s'est répandue dans le monde. Cette pandémie a un impact sur la vie quotidienne de tous les humains. Actuellement, le test de dépistage et
Program
Alliance Grants
Recipient
Location
New Brunswick, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
552039-2020
Name nom
Akhloufi, Moulay
Department département
Informatique
Organization id
73
Institution établissement
Université de Moncton
Province en
New Brunswick
Province fr
Nouveau-Brunswick
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
50000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1100
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Health, education and social services
Area of application fr
Santé publique, éducation et services sociaux
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
Detection of COVID-19 using AI - Deep learning and multiple medical imaging modalities
Keyword
NSERC
Application summary
SARS-CoV2 is a new coronavirus identified as the cause of the 2019 coronavirus disease (COVID-19) which started in Wuhan, China in late 2019 and has spread around the world. This pandemic is impacting the daily lives of everyone around the world. Currently, the most common screening and diagnostic test for COVID-19 is a laboratory test called reverse transcription polymerase chain reaction (RT-PCR). This test uses samples collected from the patient (with a nasopharyngeal and or throat swab). The specificity of this test is considered high. However, its sensitivity can be as low as 60-70%, leading to a significant number of false negatives and increasing the risk of community transmission. Other concerns are the test result long turnaround time and the worldwide shortage of reagents and swabs, which limits the number of tests that can be conducted. Experts say that for social distancing measures to be safely lifted, we will need to run a large number of tests.
Medical imaging modalities have also been used to detect signs of COVID-19. More recently, an international panel of experts evaluated the utility of imaging technologies in the management of COVID-19, especially chest radiography (CXR) and computed tomography (CT). Another interesting modality that shows a lot of promise for fast triage of new infections is chest ultrasound (CUS). Recent research shows that all these imaging modalities can be valuable in the fight against this pandemic and can be used along with RT-PCR laboratory tests. The main objective of this project is to develop an AI-based tool capable of detecting COVID-19 using medical imaging modalities of the chest (radiography (CXR), ultrasonography (CUS) and computerized tomography (ChCT)). In addition, we will develop an explainability algorithm to give a visual feedback showing the location of the signs of the disease. This visual feedback will be very useful in helping the confirmation and the stage of the disease by a health practitioner.
_x000d_Le virus SRAS-CoV2 responsable de la maladie à coronavirus COVID-19 a débuté à Wuhan, en Chine, à la fin de 2019 et s'est répandue dans le monde. Cette pandémie a un impact sur la vie quotidienne de tous les humains. Actuellement, le test de dépistage et
