A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning.

ABSTRACT: In vitro fertilization (IVF) has revolutionized infertility treatment, benefiting millions of couples worldwide. However, current clinical practices for embryo selection rely heavily on visual inspection of morphology, which is highly variable and experience dependent. Here, we propose a comprehensive artificial intelligence (AI) system that can interpret embryo-developmental knowledge encoded in vast unlabeled multi-modal datasets and provide personalized embryo selection. This AI platform consists of a transformer-based network backbone named IVFormer and a self-supervised learning framework, VTCLR (visual-temporal contrastive learning of representations), for training multi-modal embryo representations pre-trained on large and unlabeled data. When evaluated on clinical scenarios coverin

SUBMITTER: Wang G 

PROVIDER: S-EPMC11284500 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature