Current Advances in Spatial Pathology of Nasal Polyps: Challenges, Opportunities for Artificial Intelligence, and Future Directions

Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by inflammatory heterogeneity, epithelial hyperplasia, and tissue remodeling, with spatial pathology critical for deciphering pathological mechanisms and guiding clinical practice. Conventional histopathological techniques rely on subjective visual evaluation, limited by inadequate spatial resolution of molecular markers and imprecise quantitative analysis. Recent advances show that spatial transcriptomics reveals nasal polyps (NP) inflammatory heterogeneity; deep learning enables automated inflammatory subset identification and precise endotype prediction; artificial intelligence (AI)-integrated frameworks transform assessment of tissue remodeling, angiogenesis, and fibro-inflammatory niches. AI-enabled spatial pathology bridges morphological and molecular signatures, refines NP endotyping, and paves the way for targeted biologic therapies and precision care. This review summarizes progress in conventional and emerging spatial pathology approaches for NP, focusing on AI-enabled tools and their clinical translation potential.