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

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.