Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profilingSchwitzkowski, Malte (Avtor) Veeranki, Sai Pavan Kumar (Avtor) Seidel, Benedikt N. (Avtor) Kindle, Gerhard (Avtor) Rusch, Stephan (Avtor) Kramer, Diether (Avtor) Seidel, Markus G. (Avtor) Avčin, Tadej (Sodelavec pri raziskavi) Blazina, Štefan (Sodelavec pri raziskavi) Meško Meglič, Karmen (Sodelavec pri raziskavi) Kopač, Peter (Sodelavec pri raziskavi) Markelj, Gašper (Sodelavec pri raziskavi) inborn error of immunityIEIprimary immune regulatory disorderPIRDphenotype-driven disease classificationinteroperable patient dataimmune deficiency and dysregulation activity (IDDA) scoreartificial intelligenceAIunsupervised and supervised machine learningMLprimary immune disorderPIDBackground Immune dysregulation, including autoimmunity, autoinflammation, allergy, and malignancy predisposition, adds significant disease burden in primary immune disorders (PID) and inborn errors of immunity (IEIs). Objective We evaluated whether the 5-graded immune deficiency and dysregulation activity (IDDA2.1) score, encompassing 21 organ involvement and disease burden parameters, supports diagnosis across a wide spectrum of IEIs. Methods From April 2022 to November 2024, collaborators from 84 centers collected 1,043 IDDA score datasets from 825 patients across 89 IEIs (17 disorders with ≥10 patients each; range, 1-196 per IEI), including 177 scores from 141 treated patients. Supervised machine learning models ( k -nearest neighbors, support vector machine, logistic regression, random forest) classified patients into disease groups and ranked corresponding predictive features, while unsupervised uniform manifold approximation and projection (UMAP) visualized disease-specific clustering. Results Feature analysis reflected clinicians’ recognition of IEI patterns and confirmed internal IDDA score consistency. Phenotype profiles in treated patients remained informative, inversely reflecting anticipated treatment-dependent phenotype amelioration. UMAP effectively distinguished IEIs by IDDA2.1 profiles. Genetic disorder prediction achieved 73% overall accuracy, 70% for the correct monogenic IEI, and 93% within the top 3 predictions; classification reached 43% for IEI–International Union of Immunological Society categories and 59% for 12 “cardinal” IEIs (25 genes). Conclusions Random forest feature importance analysis can inform targeted clinical screening for key disease manifestations. The top 3 prediction approach demonstrates diagnostic potential, but improved accuracy will require larger, globally shared datasets. Small sample sizes for rare diseases highlight the necessity of broader collaboration to enhance AI-assisted clinical decision-making in the future.20262026-04-08 09:53:17Neznano28803sl