Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.

Journal: Reproductive biology and endocrinology : RB&E
Published Date: May 22, 2024

Abstract

BACKGROUND: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example.

Authors

  • Jiaqi Wang
  • Yufei Jin
    College of Information Engineering, China Jiliang University, Hangzhou, China. Electronic address: s20030812005@cjlu.edu.cn.
  • Aojun Jiang
    Department of Mechanical Engineering, University of Toronto, Toronto, Canada.
  • Wenyuan Chen
    Department of Mechanical Engineering, University of Toronto, Toronto, Canada.
  • Guanqiao Shan
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada.
  • Yifan Gu
    Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
  • Yue Ming
    Beijing Key Laboratory of Work Safety and Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
  • Jichang Li
    School of Medicine, The Chinese University of Hong Kong, Shenzhen, China.
  • Chunfeng Yue
    Suzhou Boundless Medical Technology Ltd., Co., Suzhou, China.
  • Zongjie Huang
    Suzhou Boundless Medical Technology Ltd., Co., Suzhou, China.
  • Clifford Librach
  • Ge Lin
    National Engineering Research Center of Digital Life, Sun Yat-Sen University, Guangzhou, China.
  • Xibu Wang
    The 3rd Affiliated Hospital of Shenzhen University, Shenzhen, China.
  • Huan Zhao
    State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Yu Sun
    Department of Neurology, China-Japan Friendship Hospital, Beijing, China.
  • Zhuoran Zhang

Keywords

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