Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study.

Journal: Journal of hypertension
PMID:

Abstract

INTRODUCTION: Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnancies.

Authors

  • Tianfan Zhou
    Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University.
  • Shengyi Gu
    Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University.
  • Feixue Shao
    Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University.
  • Ping Li
    Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Yuelin Wu
    Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University.
  • Jianhao Xiong
    Beijing Tulip Partners Technology Co., Ltd, Beijing, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Chenchen Zhou
    Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
  • Peng Gao
    Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, United States.
  • Xiaolin Hua
    Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University.