Label-Free Blood Typing by Raman Spectroscopy and Artificial Intelligence

Emil Alstrup Jensen, Murat Serhatlioglu, Cihan Uyanik, Anne Todsen Hansen, Sadasivan Puthusserypady, Morten Hanefeld Dziegiel*, Anders Kristensen*

*Corresponding author af dette arbejde

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.

OriginalsprogEngelsk
Artikelnummer2301462
TidsskriftAdvanced Materials Technologies
Vol/bind9
Udgave nummer2
Sider (fra-til)1-16
Antal sider16
DOI
StatusUdgivet - 22 jan. 2024

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