Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model.

Journal: BMC pregnancy and childbirth
PMID: 40158168

Abstract

OBJECTIVE: The study developed an intelligent online evaluation system for mediolateral episiotomy, which incorporated machine learning algorithms and integrated maternal physiological data collected during delivery.

Authors

  • Tingting Hu
    People's Hospital of Deyang City, Deyang, 618000, Sichuan, China.
  • Liheng Zhao
    Chengdu Jincheng College, Chengdu, Sichuan, 610000, China.
  • Xueling Zhao
    Chengdu University of Technology, Chengdu, Sichuan, 610000, China.
  • Lin He
    College of Plant Protection, Southwest University, Chongqing, China.
  • Xiaoli Zhong
    People's Hospital of Deyang City, Deyang, 618000, China.
  • Zhe Yin
    National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China.
  • Junjie Chen
    College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
  • Yanting Han
    Medicine and Engineering Interdisciplinary Research Laboratory of Nursing & Materials, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China. yanthan@126.com.
  • Ka Li
    Medicine and Engineering Interdisciplinary Research Laboratory of Nursing & Materials, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China. lika127@126.com.

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