Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning
This study presents a significant advancement in predicting live birth outcomes prior to embryo transfer, moving beyond traditional assessments. The findings underscore the potential of machine learning to improve clinical decision-making and enhance patient counseling in ARTs.
Authors
- Zhiyun Wei (ORCID: https://orcid.org/0000-0002-3554-4142)
- Yongdong Wu (ORCID: https://orcid.org/0000-0002-0850-724X)
- Yongyong Ren (ORCID: https://orcid.org/0000-0001-9217-3483)
- Hui Juan Lu (ORCID: https://orcid.org/0000-0001-8347-0830)
- Xinbo Wang (ORCID: https://orcid.org/0000-0001-8524-9662)
- Hao Shen (ORCID: https://orcid.org/0009-0004-8864-8844)
- Kunming Li (ORCID: https://orcid.org/0000-0002-4636-9634)
- Mei Zhao (ORCID: https://orcid.org/0000-0002-2653-3142)
- Haitao Song
- Yuechen Liu (ORCID: https://orcid.org/0000-0002-1676-8239)
- Shengnan Wu
Institutions
- Shanghai Academy of Social Sciences (CN)
- Shanghai Jiao Tong University (CN)
- Jianghan University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Shanghai Institute of Applied Physics (CN)
- Shanghai Tenth People's Hospital (CN)
- Obstetrics and Gynecology Hospital of Fudan University (CN)
- Shanghai Academy of Environmental Sciences (CN)
- Shanghai First Maternity and Infant Hospital (CN)
- Shanghai Children's Hospital (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1186/s12967-025-07045-6
- Citations
- 2
- Primary Topic
- Reproductive Health and Technologies
- Type
- article
- Field-Weighted Citation Impact
- 3.40
Funders
- Shanghai Municipal Health Commission
- Science and Technology Commission of Shanghai Municipality
- Shanghai Jiao Tong University
- National Key Research and Development Program of China