Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study
The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.
Authors
- Yuzhuo Liu (ORCID: https://orcid.org/0000-0002-7942-1769)
- Tengyu Yang (ORCID: https://orcid.org/0000-0003-0635-7089)
- Surita Aodeng
- Wei Lv (ORCID: https://orcid.org/0000-0003-0127-8824)
- Yang Zha (ORCID: https://orcid.org/0000-0003-3734-9276)
- Zhenzhen Zhu (ORCID: https://orcid.org/0000-0003-3245-3306)
- Ning Mao (ORCID: https://orcid.org/0000-0001-7097-5530)
- Haicheng Zhang (ORCID: https://orcid.org/0000-0002-8668-2799)
- Zijun Qiu
- Hang Qi
- Jingjing Li (ORCID: https://orcid.org/0000-0001-8693-387X)
- Weiqing Wang (ORCID: https://orcid.org/0000-0002-9440-1117)
- Xiaowei Wang (ORCID: https://orcid.org/0000-0001-8333-9517)
- Yuxi Lin (ORCID: https://orcid.org/0000-0001-7210-2557)
- Xicheng Song (ORCID: https://orcid.org/0000-0002-9789-1318)
- Qiao Hong
- Lei Wang (ORCID: https://orcid.org/0000-0001-8686-8764)
Institutions
- Qingdao University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Yuhuangding Hospital (CN)
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1002/alr.23595
- Citations
- 1
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 1.85
Funders
- National Natural Science Foundation of China