Abstract
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.
Original language | English |
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Article number | 2301462 |
Journal | Advanced Materials Technologies |
Volume | 9 |
Issue number | 2 |
Number of pages | 16 |
ISSN | 2365-709X |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Publisher Copyright:© 2023 The Authors. Advanced Materials Technologies published by Wiley-VCH GmbH.
Keywords
- blood typing
- machine/deep learning
- micro-capillary fluidics
- precision transfusion medicines
- Raman spectroscopy