Label-Free Blood Typing by Raman Spectroscopy and Artificial Intelligence
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Label-free blood typing by Raman spectroscopy (RS) is demonstrated by training an artificial intelligence (AI) model on 271 blood typed donor whole blood samples. A fused silica micro-capillary flow cell enables fast generation of a large dataset of Raman spectra of individual donors. A combination of resampling methods, machine learning and deep learning is used to classify the ABO blood group, 27 erythrocyte antigens, 4 platelet antigens, regular anti-B titers of blood group A donors, regular anti-A,-B titers of blood group O donors, and ABH-secretor status, from a single Raman spectrum. The average area under the curve value of the ABO classification is 0.91 ± 0.03 and 0.72 ± 0.09, respectively, for the remaining traits. The classification performance of all parameters is discussed in the context of dataset balance and antigen concentration. Post-hoc scalability analysis of the models shows the potential of RS and AI for future applications in transfusion medicine and blood banking.
Originalsprog | Engelsk |
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Artikelnummer | 2301462 |
Tidsskrift | Advanced Materials Technologies |
Vol/bind | 9 |
Udgave nummer | 2 |
Antal sider | 16 |
ISSN | 2365-709X |
DOI | |
Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
This work was supported by the NovoNordisk Foundation (Grant reference numbers 18OC0034948 and NNF0070888). The authors acknowledge Danielle Poulsen (Copenhagen University Hospital) for reference data extraction.
Publisher Copyright:
© 2023 The Authors. Advanced Materials Technologies published by Wiley-VCH GmbH.
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