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Objective assessment of stored blood quality by deep learning / Minh Doan, Joseph A. Sebastian, Juan C. Caicedo, Stefanie Siegert, Aline Roch, Tracey R. Turner, Olga Mykhailova, Ruben N. Pinto, Claire McQuin, Allen Goodman, Michael J. Parsons, Olaf Wolkenhauer, Holger Hennig, Shantanu Singh, Anne Wilson, Jason P. Acker, Paul Rees, Michael C. Kolios, Anne E. Carpenter
Proceedings of the National Academy of Sciences, Volume: 117, Issue: 35, Pages: 21381 - 21390
Swansea University Author: Paul Rees
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Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we...
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Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.
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