Stefan Prendić: Can Algorithms Support Antibody Identification in Immunohematology?
Stefan Prendić, Quality Control Specialist at Bio-Rad Medical Diagnostics GmbH, shared a post on LinkedIn:
”Can algorithms support antibody identification in immunohematology?
A recent study published in Transfusion (2026) caught my attention.
Researchers developed IrregulAB, an open-source, rule-based tool designed to support the interpretation of red blood cell antibody identification panels.
The system applies traditional serological principles, including antigen exclusion, dosage effects, and enzyme-phase reactivity, to identify possible antibody combinations.
Key findings:
The study evaluated 143 antibody identification panels using Bio-Rad gel-card technology.
In the independent validation group of 35 panels:
91 percent retained all reference antibodies during the initial screening stage.
89 percent produced at least one valid explanatory antibody combination.
However, the study also revealed important limitations, particularly with variable antigen expression, dosage effects involving anti-P1 and anti-M, and atypical reaction patterns.
For me, this highlights an important aspect of modern laboratory medicine:
Automation can improve consistency and support quality assurance, but expert interpretation remains essential.
What makes this research particularly interesting is its transparent, rule-based approach. Unlike a black-box system, its decision logic can be examined and evaluated.
As laboratory technologies continue to evolve, I believe the combination of automation, reliable reagents, and professional expertise will play an increasingly important role in transfusion medicine.”
Title: IrregulAB: An open-source rule-based engine for automated red cell antibody panel interpretation
Authors: Davide Crucitti, Violeta Vidal Ballester, Jesús Gómez Fernández, Eugenia Fernandez Mellid, Angie Lizeth Ruge Perdomo, Carlos Pérez Míguez, Adrián Mosquera Orgueira
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