| Title: | Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling |
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| Authors: | ID Schwitzkowski, Malte (Author)
ID Veeranki, Sai Pavan Kumar (Author)
ID Seidel, Benedikt N. (Author)
ID Kindle, Gerhard (Author)
ID Rusch, Stephan (Author)
ID Kramer, Diether (Author)
ID Seidel, Markus G. (Author)
ID Avčin, Tadej (Research coworker)
ID Blazina, Štefan (Research coworker)
ID Meško Meglič, Karmen (Research coworker)
ID Kopač, Peter (Research coworker)
ID Markelj, Gašper (Research coworker), et al. |
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| Files: |  PDF - Presentation file, download (2,83 MB)
MD5: AA4EFF3B00894DD354F46B92F5EC2D14
 URL - Source URL, visit https://www.jacionline.org/article/S0091-6749(25)01112-1/fulltext
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  UKC LJ - Ljubljana University Medical Centre
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| Abstract: | Background 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. |
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| Keywords: | inborn error of immunity, IEI, primary immune regulatory disorder, PIRD, phenotype-driven disease classification, interoperable patient data, immune deficiency and dysregulation activity (IDDA) score, artificial intelligence, AI, unsupervised and supervised machine learning, ML, primary immune disorder, PID |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 470-485 |
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| Numbering: | Vol. 157, issue 2 |
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| PID: | 20.500.12556/DiRROS-28803  |
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| UDC: | 616-097:004.8 |
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| ISSN on article: | 1097-6825 |
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| DOI: | 10.1016/j.jaci.2025.10.022 |
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| COBISS.SI-ID: | 262513411 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 7. 4. 2026;
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| Publication date in DiRROS: | 08.04.2026 |
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| Views: | 139 |
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| Downloads: | 101 |
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