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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 Orcid Logo, 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 Orcid Logo

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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...

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Published in: Proceedings of the National Academy of Sciences
ISSN: 0027-8424 1091-6490
Published: Proceedings of the National Academy of Sciences 2020
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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. 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spelling 2021-12-02T08:31:02.1817840 v2 55200 2020-09-17 Objective assessment of stored blood quality by deep learning 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2020-09-17 EAAS 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. Journal Article Proceedings of the National Academy of Sciences 117 35 21381 21390 Proceedings of the National Academy of Sciences 0027-8424 1091-6490 1 9 2020 2020-09-01 10.1073/pnas.2001227117 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University BBSRC, CIHR BB/N005163, BB/P026818/1, 315271, 2021-12-02T08:31:02.1817840 2020-09-17T17:03:30.7532104 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Minh Doan 1 Joseph A. Sebastian 2 Juan C. Caicedo 3 Stefanie Siegert 4 Aline Roch 5 Tracey R. Turner 6 Olga Mykhailova 7 Ruben N. Pinto 8 Claire McQuin 9 Allen Goodman 10 Michael J. Parsons 11 Olaf Wolkenhauer 12 Holger Hennig 13 Shantanu Singh 14 Anne Wilson 15 Jason P. Acker 16 Paul Rees 0000-0002-7715-6914 17 Michael C. Kolios 18 Anne E. Carpenter 19 55200__18194__f75d240f05054dcea22792a2fc8de151.pdf 55200.pdf 2020-09-17T17:05:03.3516525 Output 1937701 application/pdf Version of Record true 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). true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Objective assessment of stored blood quality by deep learning
spellingShingle Objective assessment of stored blood quality by deep learning
Paul Rees
title_short Objective assessment of stored blood quality by deep learning
title_full Objective assessment of stored blood quality by deep learning
title_fullStr Objective assessment of stored blood quality by deep learning
title_full_unstemmed Objective assessment of stored blood quality by deep learning
title_sort Objective assessment of stored blood quality by deep learning
author_id_str_mv 537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv 537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Paul Rees
author2 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
format Journal article
container_title Proceedings of the National Academy of Sciences
container_volume 117
container_issue 35
container_start_page 21381
publishDate 2020
institution Swansea University
issn 0027-8424
1091-6490
doi_str_mv 10.1073/pnas.2001227117
publisher Proceedings of the National Academy of Sciences
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
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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.
published_date 2020-09-01T04:49:27Z
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