Artificial intelligence-assisted type 2 inflammatory endotyping in CRSwNP: from Sinus CT and digital pathology to biologic decision support.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous rhinologic disease frequently driven by type 2 inflammation, yet routine clinical phenotyping remains insufficient for endotype-informed management. Conventional markers, including blood eosinophils, serum immunoglobulin E (IgE), fractional exhaled nitric oxide, tissue eosinophilia, nasal polyp score, and sinus computed tomography (CT) scores, provide useful but incomplete information. Artificial intelligence (AI) enables integration of sinus CT, radiomic features, digital pathology, biomarkers, comorbidities, and follow-up treatment outcomes. Emerging studies suggest that CT-based AI supports non-invasive identification of eosinophilic or type 2-high disease, whereas digital pathology links routine histology with inflammatory and molecular endotypes. However, published evidence remains dominated by retrospective single-modality studies or partial multi-source integration, and no model has yet jointly integrated sinus CT, digital whole-slide pathology, and biomarkers in CRSwNP. Additional limitations include heterogeneous endotype labels, insufficient external validation, and uncertain clinical utility for biologic decision-making. This Mini Review synthesizes existing single-modality artificial intelligence pipelines, contextualizes CRSwNP as a representative disease model for AI-driven type 2 inflammatory endotyping, and proposes a clinically oriented, multimodal interpretable scoring framework integrating sinus imaging and digital pathology to support precision type 2 stratification in CRS management.