Deep learning-based prediction of treatment prognosis from nasal polyp histology slides.

Journal: International forum of allergy & rhinology
PMID: 36066094

Abstract

BACKGROUND: Histopathology of nasal polyps contains rich prognostic information, which is difficult to extract objectively. In the present study, we aimed to develop a prognostic indicator of patient outcomes by analyzing scanned conventional hematoxylin and eosin (H&E)-stained slides alone using deep learning.

Authors

  • Kanghua Wang
    Department of Otolaryngology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
  • Yong Ren
    Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
  • Ling Ma
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.
  • Yunping Fan
    Department of Otolaryngology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
  • Zheng Yang
    Sichuan University - Pittsburgh Institute (SCUPI), Sichuan University, Chengdu, 610207, China.
  • Qintai Yang
    Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. Electronic address: yang.qt@163.com.
  • Jianbo Shi
  • Yueqi Sun
    Otorhinolaryngology Hospital, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

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