Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.

ABSTRACT: Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. Here, we developed and validated a deep learning model based on self-supervised contrastive learning. The model was developed with a new approach based on matched KID (Known Implantation Data) embryos derived from the same cohort of a stimulation cycle, both judged to be of good quality according to classical morphological criteria and morphokinetics, transferred fresh or frozen, but with a different implantation fate (clinical pregnancy vs. failure of implantation). We used self-supervised contrastive learning to train convolutional neural networks to ensure an unbiased and comprehensive learning of the morphokinetics features of the embry

SUBMITTER: Boucret L 

PROVIDER: S-EPMC12317125 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature