title: Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification.
abstract: In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL phenotype is crucial and hence constitutes the bottleneck of standard immunohaematology. The current method of choice, adsorption-elution, does not provide unambiguous results. We have developed a complementary method of high sensitivity that allows reliable identification of D antigen expression. Here, we present a workflow composed of high-resolution fluorescence microscopy, image processing, and machine learning that - for the first time - enables the identification of even small amounts of D antigen on the cellular level. The high sensitivity of our technique captures the full range of D antigen expression (including D+, weak D, DEL, D-), allows automated population analyses, and results in classification test accuracies of up to 96%, even for very low expressed phenotypes.
authors: University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232 Hagenberg, Austria.
authors: Daniela M
authors: DM
authors: Borgmann
authors: University of Applied Sciences Upper Austria, School of Applied Health and Social Sciences, Garnisonstrasse 21, 4020 Linz, Austria.
authors: Sandra
authors: S
authors: Mayr
authors: Red Cross Transfusion Service for Upper Austria, Krankenhausstrasse 7, 4020 Linz, Austria.
authors: Helene
authors: H
authors: Polin
authors: University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232 Hagenberg, Austria.
authors: Susanne
authors: S
authors: Schaller
authors: University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232 Hagenberg, Austria.
authors: Viktoria
authors: V
authors: Dorfer
authors: University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232 Hagenberg, Austria.
authors: Lisa
authors: L
authors: Obritzberger
authors: University of Applied Sciences Upper Austria, School of Applied Health and Social Sciences, Garnisonstrasse 21, 4020 Linz, Austria.
authors: Tanja
authors: T
authors: Endmayr
authors: Red Cross Transfusion Service for Upper Austria, Krankenhausstrasse 7, 4020 Linz, Austria.
authors: Christian
authors: C
authors: Gabriel
authors: University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232 Hagenberg, Austria.
authors: Stephan M
authors: SM
authors: Winkler
authors: University of Applied Sciences Upper Austria, School of Applied Health and Social Sciences, Garnisonstrasse 21, 4020 Linz, Austria.
authors: Jaroslaw
authors: J
authors: Jacak
mesh_terms: D004912:Erythrocytes / Q000378:metabolism; D006801:Humans; D000069550:Machine Learning; D008856:Microscopy, Fluorescence; D010641:Phenotype; D012204:Rh-Hr Blood-Group System / Q000097:blood / Q000145:classification*; D018029:Rho(D) Immune Globulin / Q000378:metabolism; D013223:Statistics as Topic
chemical_list: C061961:RHO(D) antibody; D012204:Rh-Hr Blood-Group System; D018029:Rho(D) Immune Globulin; C026410:Rho(D) antigen