Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study.

Qingsong XiQiyu YangMeng WangBo HuangBo ZhangZhou LiShuai LiuLiu YangLixia ZhuLei Jin

Published in: Reproductive biology and endocrinology : RB&E (2021)

Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Keyphrases

artificial intelligence
machine learning
big data
pregnancy outcomes
deep learning
preterm birth
metabolic syndrome
skeletal muscle