Scrutinizing high-risk patients from ASC-US cytology via a deep learning model.

Journal: Cancer cytopathology
Published Date: Jun 1, 2022

Abstract

BACKGROUND: Atypical squamous cells of undetermined significance (ASC-US) is the most frequent but ambiguous abnormal Papanicolaou (Pap) interpretation and is generally triaged by high-risk human papillomavirus (hrHPV) testing before colposcopy. This study aimed to evaluate the performance of an artificial intelligence (AI)-based triage system to predict ASC-US cytology for cervical intraepithelial neoplasia 2+ lesions (CIN2+).

Authors

  • Xiang Tao
    Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
  • Xiao Chu
    Ping An Healthcare Technology, Shanghai, China.
  • Bingxue Guo
    Ping An Healthcare Technology, Shanghai, China.
  • Qiuzhi Pan
    Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
  • Shuting Ji
    Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
  • Wenjie Lou
    Ping An Healthcare Technology, Shanghai, China.
  • Chuanfeng Lv
    Ping An Healthcare Technology, Shang Hai, PR China.
  • Guotong Xie
    Ping An Health Technology, Beijing, China.
  • Keqin Hua
    Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University.

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