Development and validation of a machine learning model for prediction of cephalic dystocia.

Journal: BMC pregnancy and childbirth
Published Date: Aug 18, 2025

Abstract

BACKGROUND: Early detection of cephalic dystocia is challenging, and current clinical assessment tools are limited. Machine learning offers unique advantages, enabling the generation of predictive models using various types of clinical data. Our model aims to integrate objective ultrasound data with psychological and sociological characteristics and obstetric treatment data to predict the individual probability of cephalic dystocia in pregnant women.

Authors

  • Yumei Huang
    Department of Obstetrics and Gynecology, Dongguan Maternal and Child Health Care Hospital, No. 99, Zhenxing Road, Zhushan Community, Dongcheng District, Dongguan, 523000, China.
  • Xuerong Ran
    Department of Midwifery, School of Nursing, Southern Medical University, No. 1023 Sha Tai South Road, Baiyun District, Guangzhou, 510515, Guangdong, China.
  • Xueyan Wang
  • Defang Wu
    Department of Obstetrics and Gynecology, Dongguan Maternal and Child Health Care Hospital, No. 99, Zhenxing Road, Zhushan Community, Dongcheng District, Dongguan, 523000, China.
  • Zheng Yao
    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China. by1403130@buaa.edu.cn.
  • Jinguo Zhai
    Department of Midwifery, School of Nursing, Southern Medical University, No. 1023 Sha Tai South Road, Baiyun District, Guangzhou, 510515, Guangdong, China. helenjxzhai@gmail.com.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals