ArticleInternational forum of allergy & rhinology2026
Development of Artificial Intelligence for Quantitative Assessment of Nasal Inflammatory Cytology in Chronic Rhinitis by Whole-Slide Images.
Article in International forum of allergy & rhinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Nasal Cytology in Precision Rhinology: From Local Cellular Phenotyping to Personalized Management of Nasal Inflammatory Diseases.Journal of personalized medicine · 2026Review
- Toward a Cytological Classification of Nasal Biofilms in Upper Airway Inflammation: Microscopic Patterns, Clinical Meaning, and Therapeutic Implications.Biomedicines · 2026Article
- Local inflammation as a determinant of recurrence in CRSwNP: the clinical value of nasal cytology.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
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10 authors.
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Abstract
backgroundChronic rhinitis (CR) is currently recognized as a syndrome that manifests in different phenotypes. We aimed to establish an artificial intelligence system (quantitative assessment of nasal inflammatory cytology, QANIC) on the basis of whole-slide images (WSIs) to enable quantitative assessment of nasal inflammatory cells.
methodsDuring the development phase of QANIC, we screened nasal secretion smears from 145 CR patients for deep learning and obtaining a robust model. Subsequently, QANIC was applied to an internal cohort (N = 881) and an independent external validation cohort comprising two clinical centers (N = 234). Cluster analysis was employed to analyze two inflammatory variables (nasal and blood eosinophil [Eos] percentages) to investigate the clinical characteristics and inflammatory patterns of different clusters.
resultsThree clusters of inflammatory phenotypes were defined in CR patients: Cluster 1 (high nasal and high blood Eoss, accounted for 17.14% and 16.24% in the two cohorts, respectively), Cluster 2 (high nasal but low blood Eoss, 45.86% and 45.30%), and Cluster 3 (low nasal and low blood Eoss, 37.00% and 38.46%). Compared to Cluster 3, Clusters 1 and 2 demonstrated more severe clinical symptoms and nasal Type 2 inflammation, along with a diagnostic advantage in identifying seasonal allergic rhinitis.
conclusionsThe QANIC marks the first time deep learning has been combined with WSIs for nasal cytology diagnosis. Subtyping rhinitis patients based on nasal cytology play an important role in monitoring inflammation dynamics and individualizing treatment.
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