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Development Of Artificial Intelligence For Quantitative Assessment Of Nasal Inflammatory Cytology In Chronic Rhinitis By Whole-slide Images.

Home
Development Of Artificial Intelligence For Quantitative Assessment Of Nasal Inflammatory Cytology In Chronic Rhinitis By Whole-slide Images.

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Development of Artificial Intelligence for Quantitative Assessment of Nasal Inflammatory Cytology in Chronic Rhinitis

Xu Zhang^1,2,3, Xu Xu^1,2,3,4, Weiwei Liu⁵

¹Department of Allergy, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

International Forum of Allergy & Rhinology

|November 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

An AI system, quantitative assessment of nasal inflammatory cytology (QANIC), uses whole-slide images to subtype chronic rhinitis patients. This approach aids in monitoring inflammation and personalizing treatment for better patient outcomes.

Keywords:

artificial intelligence chronic rhinitis deep learning inflammatory phenotype nasal cytology

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Area of Science:

Computational pathology
Immunology
Otorhinolaryngology

Background:

Chronic rhinitis (CR) presents diverse phenotypes.
Accurate assessment of nasal inflammatory cells is crucial for CR management.
Current diagnostic methods may not fully capture inflammatory nuances.

Purpose of the Study:

To develop an artificial intelligence system, quantitative assessment of nasal inflammatory cytology (QANIC), for quantitative assessment of nasal inflammatory cells using whole-slide images (WSIs).
To subtype chronic rhinitis patients into distinct inflammatory phenotypes.
To investigate the clinical characteristics and inflammatory patterns associated with these phenotypes.

Main Methods:

Development of the QANIC system using deep learning on nasal secretion smears from 145 CR patients.

Application of QANIC to an internal cohort (N=881) and an external validation cohort (N=234).

Cluster analysis of nasal and blood eosinophil (Eos) percentages to define inflammatory phenotypes.

Main Results:

Three distinct inflammatory phenotypes were identified: Cluster 1 (high nasal/blood Eos), Cluster 2 (high nasal/low blood Eos), and Cluster 3 (low nasal/low blood Eos).
Clusters 1 and 2 exhibited more severe clinical symptoms and Type 2 inflammation compared to Cluster 3.
These phenotypes showed diagnostic advantages in identifying seasonal allergic rhinitis.

Conclusions:

The QANIC system represents a novel integration of deep learning and WSIs for nasal cytology diagnosis.
Subtyping rhinitis patients based on cytology is vital for monitoring inflammation dynamics.
This approach facilitates individualized treatment strategies for chronic rhinitis.