Grant: Engage Grants Program
Saint Mary's University
$25,000.00
Medical Diagnosis using Raman Spectrographs and Machine Learning
FY 2017-18
Summary
MedMira is the developer and owner of Rapid Vertical Flow (RVF) Technology. The Company's rapid testapplications built on RVF Technology provide hospitals, labs, clinics and individuals with instant diagnosis fordiseases such as HIV and hepatitis C in just three easy steps. Together with a Tier II Canada Research Chair,Dr. Christa Brosseau, Medmira has identified rules based on sharpest peaks and preceding troughs in Ramanspectrographs of surrogate blood samples. Data analysis via application of these rules can accuratelydistinguish positive samples from negative samples. Such an analysis requires an experienced chemist toprocess the results from a Raman spectrometer; up to half an hour. The proposed project plans to automate thedata processing and explore the possibility of using machine learning to automatically generate the rules thatwere identified by the expert chemists. The machine learning approach is especially important for extendingtest from laboratory generated surrogate samples to complex human blood samples. Unlike the laboratorygenerated surrogate samples, Raman spectrographs of human blood samples can vary significantly from personto person. The heterogeneous blood samples may have additional peaks and troughs which could complicateinterpretation. More sophisticated machine learning techniques are more likely to replicate the diagnosticprocess facilitated by experts. The outputs of the project will enable MedMira to initiate development of toolsallowing non-experts to obtain diagnostics results in non-traditional health care settings without sacrificingquality.
Program
Engage Grants Program
Recipient
Location
Nova Scotia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
521157-2017
Name nom
Lingras, Pawan
Department département
Mathematics and Computing Science
Organization id
85
Institution établissement
Saint Mary's University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
25000
Program id
EGP
Program name en
Engage Grants Program
Program name fr
Programme de subventions d'engagement partenariat
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1551
Committee name en
Atlantic Internal Decision Committee
Committee name fr
Comité de décision interne pour l'Atlantique
Area of application code
1211
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Medical and health sciences
Area of application fr
Sciences médicales et de la santé
Research subject code
2805
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Learning and inference theories
Research subject fr
Théories de l'apprentissage et l'inférence
Partie
1
Nb partie
1
Application title
Medical Diagnosis using Raman Spectrographs and Machine Learning
Keyword
NSERC
Application summary
MedMira is the developer and owner of Rapid Vertical Flow (RVF) Technology. The Company's rapid testapplications built on RVF Technology provide hospitals, labs, clinics and individuals with instant diagnosis fordiseases such as HIV and hepatitis C in just three easy steps. Together with a Tier II Canada Research Chair,Dr. Christa Brosseau, Medmira has identified rules based on sharpest peaks and preceding troughs in Ramanspectrographs of surrogate blood samples. Data analysis via application of these rules can accuratelydistinguish positive samples from negative samples. Such an analysis requires an experienced chemist toprocess the results from a Raman spectrometer; up to half an hour. The proposed project plans to automate thedata processing and explore the possibility of using machine learning to automatically generate the rules thatwere identified by the expert chemists. The machine learning approach is especially important for extendingtest from laboratory generated surrogate samples to complex human blood samples. Unlike the laboratorygenerated surrogate samples, Raman spectrographs of human blood samples can vary significantly from personto person. The heterogeneous blood samples may have additional peaks and troughs which could complicateinterpretation. More sophisticated machine learning techniques are more likely to replicate the diagnosticprocess facilitated by experts. The outputs of the project will enable MedMira to initiate development of toolsallowing non-experts to obtain diagnostics results in non-traditional health care settings without sacrificingquality.