Development of Artificial Intelligence for Quantitative Assessment of Nasal Inflammatory Cytology in Chronic Rhinitis by Whole‐Slide Images
The 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.
Authors
- Long Qin
- Chengshuo Wang (ORCID: https://orcid.org/0000-0003-0646-5135)
- Luo Zhang (ORCID: https://orcid.org/0000-0002-0910-9884)
- Xu Xu (ORCID: https://orcid.org/0009-0003-5844-6208)
- Weiwei Liu (ORCID: https://orcid.org/0000-0003-4708-8057)
- Xu Zhang (ORCID: https://orcid.org/0000-0002-4356-0281)
- Jingyun Li (ORCID: https://orcid.org/0000-0003-4951-4320)
- Yuan Zhang (ORCID: https://orcid.org/0000-0003-1080-4267)
- Yu Song (ORCID: https://orcid.org/0000-0002-5965-0636)
- Xi Lin (ORCID: https://orcid.org/0009-0000-1299-9008)
Institutions
- Beijing Tongren Hospital (CN)
- Capital Medical University (CN)
- Cangzhou Central Hospital (CN)
- Beijing Municipal Education Commission (CN)
- Baotou Medical College (CN)
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1002/alr.70075
- Citations
- 2
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 3.70
Funders
- National Natural Science Foundation of China
- Beijing Municipal Science and Technology Commission