Belgian researchers develop AI tool to improve diagnosis of rare immune disorders

“In diagnosing primary immunodeficiencies, speed and accuracy can make a real difference for patients,” says Prof. Yvan Saeys (VIB-UGent Center for Inflammation Research). “With PIDgeon, we combine advanced machine learning with explainable AI to quickly arrive at a potential diagnosis, which can then be assessed by clinicians.”

PIDs are rare disorders caused by defects in the immune system. Some forms, such as Severe Combined Immunodeficiency (SCID), are life-threatening if not detected early. Diagnosing these conditions requires detailed analysis of flow cytometry data, a powerful but complex laboratory technique that traditionally depends heavily on expert interpretation.

PIDgeon introduces a fully automated and transparent AI approach that makes the step from the lab to clinical settings. The system analyzes standardized flow cytometry data, identifies abnormal immune profiles, and suggests possible disease subtypes. Importantly, it provides interpretable reports that show which immune cell populations drive each prediction.

Enhance, not replace

First author Dr. Annelies Emmaneel (VIB-UGent), emphasizes the collaborative nature of the work: “We developed PIDgeon side by side with clinicians. Their feedback continuously shaped the model."

"That close interaction," adds co-first author Dr. Jana Neirinck (UGent, University Hospital Ghent), "ensured the system addresses real diagnostic questions and produces results that are clinically meaningful.”

In validation across nearly 1,000 patient samples from multiple European centers, PIDgeon achieved high accuracy in automated immune cell identification and showed very high sensitivity in detecting severe T-cell defects such as SCID.

“The explainable nature of the system is essential,” adds Prof. Carolien Bonroy, clinical biologist at Ghent University Hospital. “It will allow us to critically evaluate the results and integrate them responsibly into clinical decision-making. We look forward to applying it in practice.”

PIDgeon is designed as a decision-support and triage tool. It can rapidly analyze new patient samples and generate an interpretable report within minutes, helping experts prioritize urgent cases while increasing efficiency and consistency.

The research was supported by VIB’s Grand Challenges Program, a strategic initiative that stimulates ambitious interdisciplinary collaborations addressing major biomedical and societal challenges. By bringing together expertise in artificial intelligence, immunology, and clinical diagnostics, PIDgeon exemplifies how fundamental research can translate into practical tools that bring AI closer to routine patient care.

Prof. Yvan Saeys: “The close collaboration between researchers and clinicians within this Grand Challenges Project is what makes it so valuable and relevant.”

Publication

PIDgeon: An Explainable AI Model for Improved Flow Cytometry-Based Screening of Lymphoid Primary Immunodeficiencies. Emmaneel, Neirinck, et al. Clinical Chemistry, 2026.

Funding

This work was supported by the VIB Grand Challenges Program, the EuroFlow Consortium, the Flemish Government, FWO, and the Czech Health Research Council. ​

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