Construction of a Prediction Model for Adverse Perinatal Outcomes in Foetal Growth Restriction Based on a Machine Learning Algorithm: A Retrospective Study
The ML algorithm for APO, which integrates maternal clinical factors and ultrasound parameters, demonstrates good predictive value for APO in FGR at diagnosis. This suggested that ML techniques may be a valid approach for the early detection of high-risk APO in FGR pregnancies.
Authors
- Qingqing Wu (ORCID: https://orcid.org/0000-0002-6836-9192)
- Xiangli Meng
- Xiaofei Li (ORCID: https://orcid.org/0000-0002-9219-3502)
- Na Zhang (ORCID: https://orcid.org/0000-0001-6978-2731)
- Lei Wang (ORCID: https://orcid.org/0000-0002-7700-4531)
- Minghui Wu (ORCID: https://orcid.org/0000-0001-8179-7119)
Institutions
- Capital Medical University (CN)
- Beijing Obstetrics and Gynecology Hospital (CN)
Publication Details
- Journal
- BJOG An International Journal of Obstetrics & Gynaecology
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1111/1471-0528.18226
- Citations
- 4
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 8.23
Funders
- Beijing Municipal Health Commission
- National Key Research and Development Program of China