Artificial intelligence-powered oocyte evaluation: Correlating cytoplasmic features with blastocyst development
These findings underscore the value of cytoplasmic features as objective indicators of developmental competence. This artificial intelligence-driven approach may improve embryo selection by providing a standardized, non-invasive method for evaluating oocytes, ultimately contributing to enhanced clinical outcomes in assisted reproductive technologies.
Authors
- Hye‐Jeong Park (ORCID: https://orcid.org/0000-0001-8243-079X)
- Jun‐Xue Jin (ORCID: https://orcid.org/0000-0003-0932-2105)
- Kyungmi Juhn
- HyungMin Kim (ORCID: https://orcid.org/0000-0003-2824-0535)
- Hyosang Lee (ORCID: https://orcid.org/0009-0008-6758-9944)
- Jeong Yoon (ORCID: https://orcid.org/0009-0002-1019-4702)
- Eun‐Hye Jung
- Jin‐Ho Lim
- Hyoeun Kang
- Jin Seok Heo
Institutions
- Maria Fertility Hospital (KR)
- Korea Kacoh (South Korea) (KR)
Publication Details
- Journal
- Daehan saengsik uihak hoeji/Clinical and experimental reproductive medicine
- Published
- 2025-12-09
- DOI
- https://doi.org/10.5653/cerm.2025.08396
- Primary Topic
- Reproductive Biology and Fertility
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea Medical Device Development Fund
- Ministry of Trade, Industry and Energy
- Ministry of Food and Drug Safety
- Ministry of Science and ICT, South Korea