A machine learning model for prenatal risk prediction of cephalopelvic disproportion‐related dystocia: A retrospective study
Obstetricians' prenatal assessments based on clinical experience cannot identify all CPD-related dystocia cases, leading to emergency CDs among patients initially deemed low-risk. Our prediction model, utilizing routine clinical parameters, effectively identifies high-risk CPD-related dystocia prenatally, thereby addressing the limitations of clinical judgment.
Authors
- Su Zhang (ORCID: https://orcid.org/0000-0002-5928-6360)
- Ping Huang (ORCID: https://orcid.org/0009-0008-1865-5570)
- Xiuli Yang (ORCID: https://orcid.org/0000-0002-6912-0962)
- Liucheng Li (ORCID: https://orcid.org/0000-0002-2511-0156)
- Yu‐Mei Wang
- Weiqi Xia (ORCID: https://orcid.org/0000-0001-8213-5272)
- S Z Liu (ORCID: https://orcid.org/0009-0005-1145-5785)
- Hongjuan Jiang
- Haihui Zhou (ORCID: https://orcid.org/0009-0007-0110-5348)
Institutions
- Zhejiang Chinese Medical University (CN)
- Sir Run Run Shaw Hospital (CN)
- Zhejiang Provincial People's Hospital (CN)
- Hangzhou Medical College (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-04-02
- DOI
- https://doi.org/10.1002/ijgo.70126
- Primary Topic
- Maternal and Perinatal Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00