Clinical chemistry · 2026
PIDgeon: An Explainable AI Model for Improved Flow Cytometry-Based Screening of Lymphoid Primary Immunodeficiencies.
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Emmaneel Annelies, Neirinck Jana, Torres-Valle Alba, Van Gassen Sofie, Bonte Sarah, Buysse Malicorne, van Dongen Jacques J M, Orfao Alberto, van der Burg Mirjam, Kalina Tomas, Lambrecht Bart N, De Vriendt Ciel, Kerre Tessa, Haerynck Filomeen, Pérez-Andrés Martin, Hofmans Mattias, Bonroy Carolien, Saeys Yvan
Аннотация
BACKGROUND: Primary immunodeficiencies (PIDs) are rare disorders caused by immune system defects that are commonly screened using multi-parameter flow cytometry (FCM). To counter the subjective and time-consuming manual data analysis of FCM data, we present PIDgeon, a fully automated computational pipeline based on artificial intelligence (AI) techniques. PIDgeon is designed to characterize PID immune profiles, suggest PID subtypes based on altered immune profiles, age, and immunoglobulin levels, and generate interpretable reports.
METHODS: The PIDgeon pipeline, including FlowSOM and extreme gradient boosting models, was trained and tested on standardized FCM data generated according to EuroFlow procedures on 74 healthy controls and 399 patients (281 lymphoid-PID patients and 118 non-PID diseased controls) collected by the Ghent University Hospital. Subsequently, multi-centric validation was performed on internal (n = 211) and external (n = 338) independent data sets collected across 4 EuroFlow centers.
RESULTS: Validation demonstrated high accuracy in cell count enumeration, achieving correlation scores above 0.90 for the major lymphocyte subsets. Interestingly, PIDgeon showed high sensitivity (93% to 100%) in predicting PID with severe T-cell defects, such as severe combined immunodeficiency and late-onset combined immunodeficiency, and low false-negative rates (1.5% to 5.4%) for distinguishing other lymphoid-PID vs non-PID diseased controls across data sets. Additionally, PIDgeon gives a first hint toward prediction of subtypes of primary antibody deficiencies, such as common variable immunodeficiency.
CONCLUSIONS: In summary, PIDgeon is an accessible and explainable AI-pipeline aligned with current clinical needs, aiding laboratory immunologists in early PID diagnostics and increasing data analysis efficiency.
METHODS: The PIDgeon pipeline, including FlowSOM and extreme gradient boosting models, was trained and tested on standardized FCM data generated according to EuroFlow procedures on 74 healthy controls and 399 patients (281 lymphoid-PID patients and 118 non-PID diseased controls) collected by the Ghent University Hospital. Subsequently, multi-centric validation was performed on internal (n = 211) and external (n = 338) independent data sets collected across 4 EuroFlow centers.
RESULTS: Validation demonstrated high accuracy in cell count enumeration, achieving correlation scores above 0.90 for the major lymphocyte subsets. Interestingly, PIDgeon showed high sensitivity (93% to 100%) in predicting PID with severe T-cell defects, such as severe combined immunodeficiency and late-onset combined immunodeficiency, and low false-negative rates (1.5% to 5.4%) for distinguishing other lymphoid-PID vs non-PID diseased controls across data sets. Additionally, PIDgeon gives a first hint toward prediction of subtypes of primary antibody deficiencies, such as common variable immunodeficiency.
CONCLUSIONS: In summary, PIDgeon is an accessible and explainable AI-pipeline aligned with current clinical needs, aiding laboratory immunologists in early PID diagnostics and increasing data analysis efficiency.