Label-Free Blood Typing by Raman Spectroscopy and Artificial Intelligence

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. The ABO blood group and 33 additional blood group traits are determined label-free by Raman spectroscopy (RS) and Artificial Intelligence (AI). Whole blood is analyzed in a fused silica micro-capillary with no sample preparation steps ensuring fast generation of Raman spectra. The performance scalability of the trained AI models is discussed in the context of future applications in transfusion medicine.image