Journal article 535 views 201 downloads
Objective assessment of stored blood quality by deep learning
,
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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Copyright © 2020 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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DOI (Published version): 10.1073/pnas.2001227117
Abstract
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...
| Published in: | Proceedings of the National Academy of Sciences |
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| ISSN: | 0027-8424 1091-6490 |
| Published: |
Proceedings of the National Academy of Sciences
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa55200 |
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| Abstract: |
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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| College: |
Faculty of Science and Engineering |
| Funders: |
BBSRC, CIHR |
| Issue: |
35 |
| Start Page: |
21381 |
| End Page: |
21390 |