Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study.

Journal: International forum of allergy & rhinology
Published Date: May 19, 2025

Abstract

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions.

Authors

  • Jingjing Li
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Ning Mao
    Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, People's Republic of China.
  • Surita Aodeng
    Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Haicheng Zhang
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, People's Republic of China.
  • Zhenzhen Zhu
    Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Yuzhuo Liu
    School of Public Health, Shandong Second Medical University, Weifang, Shandong, China.
  • Hang Qi
    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Hong Qiao
    State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of SciencesBeijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence TechnologyShanghai, China; University of Chinese Academy of SciencesBeijing, China.
  • Yuxi Lin
    Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zijun Qiu
    Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Tengyu Yang
    Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Yang Zha
    Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Xiaowei Wang
    Beijing Centers for Preventive Medical Research, Beijing 100013, China.
  • Weiqing Wang
    National Clinical Research Center for Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Xicheng Song
    Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China. drxchsong@163.com.
  • Wei Lv
    Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China.

Keywords

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