ArticleScientific reports2016
Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- CRISPR/Cas9 Genome Editing vs. Over-Expression for Fluorescent Extracellular Vesicle-Labeling: A Quantitative Analysis.International journal of molecular sciences · 2021Article
- Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model.PLoS computational biology · 2020Article
- Localization Microscopy of Actin Cytoskeleton in Human Platelets.International journal of molecular sciences · 2018Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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