Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer
Leveraging advanced machine-learning models and identifying critical predictive factors can improve the accuracy of live birth outcome predictions for AMA patients undergoing SVBT. These findings offer valuable insights for enhancing clinical decision-making and managing patient expectations. Further research is needed to validate these results in larger, multi-center cohorts and to explore additional factors, including fresh embryo transfers, to broaden the applicability of these models in clinical practice.
Authors
- Ming Liao (ORCID: https://orcid.org/0000-0001-8731-4528)
- Yihua Yang (ORCID: https://orcid.org/0000-0002-5371-3843)
- Lidan Liu (ORCID: https://orcid.org/0000-0002-0369-8200)
- Qiuying Gan
- Qianyi Huang
- Bo Liu (ORCID: https://orcid.org/0000-0002-6697-9528)
Institutions
- Guangxi Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
- Guangxi Maternal and Child Health Hospital (CN)
Publication Details
- Journal
- Reproductive Biology and Endocrinology
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1186/s12958-024-01295-7
- Citations
- 6
- Primary Topic
- Reproductive Biology and Fertility
- Type
- article
- Field-Weighted Citation Impact
- 3.26
Funders
- Guangxi Zhuang Autonomous Region Health and Family Planning Commission