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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
general
Objective assessment of stored blood quality by deep learning
Proceedings of the National Academy of Sciences of the United States of America, Volume 117, No. 35, Year 2020
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Description
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. © 2020 National Academy of Sciences. All rights reserved.
Authors & Co-Authors
Doan, Minh
United States, Cambridge
Massachusetts Institute of Technology
Wolkenhauer, Olaf
Germany, Rostock
Universität Rostock
Hennig, Holger
Germany, Rostock
Universität Rostock
Acker, Jason P.
Canada, Ottawa
Canadian Blood Services
Canada, Edmonton
University of Alberta
Rees, Paul E.T.
United States, Cambridge
Massachusetts Institute of Technology
United Kingdom, Swansea
Swansea University
Carpenter, Anne Elizabeth
United States, Cambridge
Massachusetts Institute of Technology
Statistics
Citations: 48
Authors: 6
Affiliations: 12
Identifiers
Doi:
10.1073/pnas.2001227117
ISSN:
00278424
Research Areas
Cancer