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RhDnostics: A Machine Learning-Based Predictive Algorithm Model for RhD-Negative and DEL Blood Group Screening.

Meechoke Choodoung¹, Charuporn Promwong², Ketsaraporn Wongba²

¹Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, Thailand.

The Journal of Applied Laboratory Medicine

|May 30, 2025

Summary

A machine learning algorithm accurately identifies RhD-negative blood types, aiding in the screening of D-elution (DEL) red blood cells. This tool improves laboratory practice and patient safety where confirmatory testing is unavailable.

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Area of Science:

Immunology
Genetics
Computational Biology

Background:

The D-elution (DEL) phenotype is often misidentified as Rh-negative due to low D antigen expression on red blood cells.
Standard confirmation methods like adsorption-elution tests and genotyping face challenges in laboratory practice, impacting patient safety.
These challenges include long turnaround times, instrument and reagent availability, budget constraints, and technical difficulties.

Purpose of the Study:

To develop a predictive algorithm for screening D-elution (DEL) and RhD-negative blood types.
To leverage machine learning for accurate identification of these blood groups.

Main Methods:

A machine learning model was developed using serological data from RhCcEe antigen tests.
The model incorporated adsorption-elution test data for DEL confirmation.
The algorithm was trained and validated on a Thai blood donor dataset.

Main Results:

The machine learning algorithm achieved over 90% predictive accuracy for RhD-negative identification.
The algorithm demonstrated effectiveness with or without DEL confirmatory serological tests.
A web application, RhDnostics, was developed to facilitate RhD-negative screening.

Conclusions:

The developed machine learning algorithm serves as a valuable predictive tool for RhD-negative screening in laboratories.
It offers a viable solution when confirmatory serological tests or RHD molecular testing are not accessible.
This approach enhances laboratory efficiency and patient safety in blood banking.