A machine learning model for predicting fertilization following short‐term insemination using embryo images
MLM can effectively predict fertilization following short-term insemination by analyzing cytoplasmic changes in images. These results underscore the potential to enhance clinical decision-making and improve patient outcomes.
Authors
- Satoshi Kishigami (ORCID: https://orcid.org/0000-0001-9447-5100)
- Ikumi Nakajima
- Leona Matsumoto
- Yuya Yoshikawa (ORCID: https://orcid.org/0000-0001-8584-8470)
- Kazunori Matsumoto (ORCID: https://orcid.org/0000-0002-5753-0104)
- Tomoki Tanaka (ORCID: https://orcid.org/0000-0002-0977-0998)
- Chuandong Zhu (ORCID: https://orcid.org/0000-0002-8302-9444)
- Naoji MASUYA
- Shinya Fukuda
- Hirofumi Haraguchi
- Miwako Saito (ORCID: https://orcid.org/0009-0006-2437-6813)
Institutions
- University of Yamanashi (JP)
- Chiba Institute of Technology (JP)
Publication Details
- Journal
- Reproductive Medicine and Biology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1002/rmb2.12649
- Citations
- 1
- Primary Topic
- Reproductive Biology and Fertility
- Type
- article
- Field-Weighted Citation Impact
- 1.88