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Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification.

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Accurately identifying Rhesus D types, especially the rare DEL phenotype, is vital in transfusion medicine. A new high-sensitivity method using microscopy, image processing, and machine learning reliably detects D antigen expression, improving patient safety.

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Area of Science:

  • Transfusion Medicine
  • Immunohaematology
  • Biotechnology

Background:

  • Accurate Rhesus D typing is critical in transfusion medicine to prevent anti-D immunization in Rhesus D negative recipients.
  • Detecting the low-expressed DEL phenotype is a significant challenge in standard immunohaematology, as current methods like adsorption-elution lack unambiguous results.

Purpose of the Study:

  • To develop a highly sensitive and reliable method for identifying D antigen expression on red blood cells.
  • To overcome the limitations of current immunohaematology techniques for detecting weak D antigen phenotypes, particularly the DEL type.

Main Methods:

  • Development of a novel workflow integrating high-resolution fluorescence microscopy, advanced image processing, and machine learning algorithms.
  • Application of the workflow for the cellular-level identification of D antigen expression, including very low levels.
  • Automated population analysis for high-throughput screening and classification.

Main Results:

  • The new method reliably identifies the full spectrum of D antigen expression, from D+ to weak D, DEL, and D- phenotypes.
  • Achieved classification test accuracies of up to 96%, demonstrating high sensitivity even for extremely low D antigen expression.
  • Enabled automated population analyses, streamlining the identification process.

Conclusions:

  • The presented workflow offers a sensitive and reliable complementary method for Rhesus D typing, significantly improving the detection of challenging phenotypes like DEL.
  • This approach enhances transfusion safety by providing unambiguous identification of D antigen expression, crucial for preventing alloimmunization.
  • The integration of microscopy, image processing, and machine learning represents a significant advancement in immunohaematology diagnostics.