Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps.

Journal: BMC medical imaging
Published Date: Jan 24, 2024

Abstract

BACKGROUND: As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP.

Authors

  • Weidong Du
    The First Affiliated Hospital of Zhejiang, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Chinese Medical University, Hangzhou, 310006, China. doctordu20@163.com.
  • Weipiao Kang
    Department of Otolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Shantou University Medical College, 69 North Dongxia Road, 515041, Shantou, Guangdong, China.
  • Shixin Lai
    College of Engineering, Shantou University, 515063, Shantou, China.
  • Zehong Cai
    Department of Otolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Shantou University Medical College, 69 North Dongxia Road, 515041, Shantou, Guangdong, China.
  • Yaowen Chen
    College of Engineering, Shantou University, 515063, Shantou, China.
  • Xiaolei Zhang
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Key Laboratory of on Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, China.
  • Yu Lin
    Research School of Computer Science, Australian National University, Canberra, 2601, ACT, Australia.

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