A MULTICENTER INVESTIGATION OF AUTOIMMUNE DISEASE OVERLAP SYNDROMES USING INTEGRATED PATHWAY ENRICHMENT, IMAGING BIOMARKERS, AND BAYESIAN PROBABILISTIC MODELING

Abstract

Autoimmune diseases, particularly those with overlapping clinical manifestations, present significant diagnostic and prognostic challenges. This study introduces an integrated, multimodal artificial intelligence framework that synthesizes multi-omics, radiological, and clinical data to improve the diagnosis and classification of autoimmune disease overlap syndromes. Using high-throughput transcriptomic and proteomic profiling, pathway enrichment analysis, and quantitative imaging biomarkers, we constructed a comprehensive feature matrix for modeling. Machine learning algorithms, particularly Random Forest and XGBoost, demonstrated superior classification performance with accuracy up to 98%, while Bayesian networks enabled interpretable probabilistic reasoning across modalities. SHAP analysis revealed key genomic and radiomic predictors, enhancing model transparency. A 75% concordance rate between AI-driven diagnosis and clinician assessments validated the model’s real-world applicability. Visualization techniques including heatmaps, radar charts, and hybrid plots further confirmed the discriminative power of the integrated features. This integrative AI-driven methodology not only enhances diagnostic accuracy but also offers a scalable solution for complex disease phenotyping, setting a precedent for precision medicine approaches in autoimmune research.