Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation.

Journal: Chinese medical journal
Published Date: Jul 1, 2021

Abstract

BACKGROUND: Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM.

Authors

  • Tai-Hui Xia
    Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, China.
  • Man Tan
    The School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Jing-Hua Li
    Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, China.
  • Jing-Jing Wang
    Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, China.
  • Qing-Qing Wu
    Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, China.
  • De-Xing Kong
    The School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.

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