Review Article| Volume 59, ISSUE 6, 103020, December 2020

Emerging use of machine learning and advanced technologies to assess red cell quality

  • Joseph A. Sebastian
    Corresponding author at: 661 University Avenue, Toronto, ON, M5G 1X8, Canada.
    Institute of Biomedical Engineering, University of Toronto, 164 College St., Toronto, Ontario, M5S 3G9, Canada

    Translational Biology and Engineering Program, Ted Rogers Center for Heart Research, 661 University Avenue, Toronto, ON, M5G 1X8, Canada
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  • Michael C. Kolios
    Department of Physics, Ryerson University, 350 Victoria St., Toronto, Ontario, M5B 2K3, Canada

    Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Ryerson University and St. Michael’s Hospital, 209 Victoria St, Toronto, Ontario, M5B 1T8, Canada

    Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria St., Toronto, Ontario, M5B 1T8, Canada
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  • Jason P. Acker
    Centre for Innovation, Canadian Blood Services, 8249-114 St., Edmonton, Alberta, T6G 2R8, Canada

    Department of Laboratory Medicine and Pathology, University of Alberta, 8249-114 St., Edmonton, Alberta, T6G 2R8, Canada
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Published:November 21, 2020DOI:


      Improving blood product quality and patient outcomes is an accepted goal in transfusion medicine research. Thus, there is an urgent need to understand the potential adverse effects on red blood cells (RBCs) during pre-transfusion storage. Current assessment techniques of these degradation events, termed “storage lesions”, are subjective, labor-intensive, and complex. Here we describe emerging technologies that assess the biochemical, biophysical, and morphological characteristics of RBC storage lesions. Of these emerging techniques, machine learning (ML) has shown potential to overcome the limitations of conventional RBC assessment methods. Our previous work has shown that neural networks can extract chronological progressions of morphological changes in RBCs during storage without human input. We hypothesize that, with broader training and testing of multivariate data (e.g., varying donor factors and manufacturing methods), ML can further our understanding of clinical transfusion outcomes in multiple patient groups.


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