Reference number : GC-2017-Q4-01077, amendment number : , agreement type : G, recipient legal name : Lingras, Pawan (Saint Mary’s University), recipient country : CA, recipient province : nan, recipient city : Nova Scotia|Nouvelle-Écosse, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : MedMira is the developer and owner of Rapid Vertical Flow (RVF) Technology. The Company's rapid test_x000D_
applications built on RVF Technology provide hospitals, labs, clinics and individuals with instant diagnosis for_x000D_
diseases such as HIV and hepatitis C in just three easy steps. Together with a Tier II Canada Research Chair,_x000D_
Dr. Christa Brosseau, Medmira has identified rules based on sharpest peaks and preceding troughs in Raman_x000D_
spectrographs of surrogate blood samples. Data analysis via application of these rules can accurately_x000D_
distinguish positive samples from negative samples. Such an analysis requires an experienced chemist to_x000D_
process the results from a Raman spectrometer; up to half an hour. The proposed project plans to automate the_x000D_
data processing and explore the possibility of using machine learning to automatically generate the rules that_x000D_
were identified by the expert chemists. The machine learning approach is especially important for extending_x000D_
test from laboratory generated surrogate samples to complex human blood samples. Unlike the laboratory_x000D_
generated surrogate samples, Raman spectrographs of human blood samples can vary significantly from person_x000D_
to person. The heterogeneous blood samples may have additional peaks and troughs which could complicate_x000D_
interpretation. More sophisticated machine learning techniques are more likely to replicate the diagnostic_x000D_
process facilitated by experts. The outputs of the project will enable MedMira to initiate development of tools_x000D_
allowing non-experts to obtain diagnostics results in non-traditional health care settings without sacrificing_x000D_
quality., program purpose french : MedMira is the developer and owner of Rapid Vertical Flow (RVF) Technology. The Company's rapid test_x000D_
applications built on RVF Technology provide hospitals, labs, clinics and individuals with instant diagnosis for_x000D_
diseases such as HIV and hepatitis C in just three easy steps. Together with a Tier II Canada Research Chair,_x000D_
Dr. Christa Brosseau, Medmira has identified rules based on sharpest peaks and preceding troughs in Raman_x000D_
spectrographs of surrogate blood samples. Data analysis via application of these rules can accurately_x000D_
distinguish positive samples from negative samples. Such an analysis requires an experienced chemist to_x000D_
process the results from a Raman spectrometer; up to half an hour. The proposed project plans to automate the_x000D_
data processing and explore the possibility of using machine learning to automatically generate the rules that_x000D_
were identified by the expert chemists. The machine learning approach is especially important for extending_x000D_
test from laboratory generated surrogate samples to complex human blood samples. Unlike the laboratory_x000D_
generated surrogate samples, Raman spectrographs of human blood samples can vary significantly from person_x000D_
to person. The heterogeneous blood samples may have additional peaks and troughs which could complicate_x000D_
interpretation. More sophisticated machine learning techniques are more likely to replicate the diagnostic_x000D_
process facilitated by experts. The outputs of the project will enable MedMira to initiate development of tools_x000D_
allowing non-experts to obtain diagnostics results in non-traditional health care settings without sacrificing_x000D_
quality., agreement title english : Medical Diagnosis using Raman Spectrographs and Machine Learning, agreement title french : Medical Diagnosis using Raman Spectrographs and Machine Learning, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada