Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model
The early warning system for the risk of mediolateral episiotomy constructed in this study can accurately and rapidly utilize the physiological data of parturients during labor to predict the risk of mediolateral episiotomy in the third stage of labor.
Authors
- Zhe Yin
- Yanting Han (ORCID: https://orcid.org/0000-0003-3381-2059)
- Xueling Zhao (ORCID: https://orcid.org/0000-0002-0669-6999)
- Ka Li (ORCID: https://orcid.org/0000-0003-1091-1505)
- Xiaoli Zhong (ORCID: https://orcid.org/0000-0002-6505-7871)
- Junjie Chen (ORCID: https://orcid.org/0000-0003-1131-2133)
- Liheng Zhao
- Lin He
- Tingting Hu (ORCID: https://orcid.org/0000-0002-0637-6944)
Institutions
- Hong Kong Polytechnic University (HK)
- Xinjiang Medical University (CN)
- Chengdu Medical College (CN)
- Sichuan University (CN)
- Chengdu University of Technology (CN)
- Tumor Hospital of Xinjiang Medical University (CN)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1186/s12884-025-07441-2
- Citations
- 3
- Primary Topic
- Pelvic and Acetabular Injuries
- Type
- article
- Field-Weighted Citation Impact
- 6.02
Funders
- National Natural Science Foundation of China