TY - JOUR
T1 - Label-Free Blood Typing by Raman Spectroscopy and Artificial Intelligence
AU - Jensen, Emil Alstrup
AU - Serhatlioglu, Murat
AU - Uyanik, Cihan
AU - Hansen, Anne Todsen
AU - Puthusserypady, Sadasivan
AU - Dziegiel, Morten Hanefeld
AU - Kristensen, Anders
N1 - Publisher Copyright:
© 2023 The Authors. Advanced Materials Technologies published by Wiley-VCH GmbH.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Blood typing
KW - Machine/deep learning
KW - Micro-capillary fluidics
KW - Precision transfusion medicine
KW - Raman spectroscopy
U2 - 10.1002/admt.202301462
DO - 10.1002/admt.202301462
M3 - Journal article
AN - SCOPUS:85178488219
SN - 2365-709x
VL - 9
JO - Advanced Materials Technologies
JF - Advanced Materials Technologies
IS - 2
M1 - 2301462
ER -