AI-based prediction of aspirin-exacerbated respiratory disease using nasal epithelial mRNA expression profiles

Background: Aspirin-exacerbated respiratory disease (AERD) is a distinct asthma endotype marked by asthma, nasal polyposis, and respiratory reactions to COX-1 inhibitors. Early and accurate identification of AERD remains clinically challenging. Objective: We sought to develop and externally validate an artificial intelligence (AI)-based diagnostic model that uses nasal epithelial mRNA expression profiles to accurately identify AERD. Methods: cohort. Results: (Charcot-Leyden crystal protein), which are known to be important to AERD pathogenesis. Conclusions: Nasal transcriptomics can predict AERD diagnosis accurately and may improve disease understanding, enabling earlier and more precise endotype-based diagnosis and management.