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

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 covering the entire IVF cycle, our pre-trained AI model demonstrates accurate and reliable performance on euploidy ranking and live-birth occurrence prediction. For AI vs. physician for euploidy ranking, our model achieved superior performance across all score categories. The results demonstrate the potential of the AI system as a non-invasive, efficient, and cost-effective tool to improve embryo selection and IVF outcomes. [Display omitted] •The embryo-selection process in the IVF cycle is variable and experience dependent•Current AI emphasizes embryo-selection tasks with limited clinical applicability•Our system makes full use of multi-modal and unlabeled data using contrastive learning•Our system accurately and reliably predicts the embryo status and live-birth outcome In the in vitro fertilization (IVF) process, embryos are usually selected based on morphological characteristics or genetic test results, which are highly variable, experience dependent, and time consuming. To tackle data heterogeneity and labeling limitations, we propose an artificial intelligence (AI) framework system that evaluates embryo images and videos during the assessment of the IVF cycle. This research highlights the potential of AI models to serve as non-invasive, efficient, and cost-effective tools for the advancement of reproductive medicine in general, but specifically for embryo-selection tasks during IVF. In this paper, the authors developed a unified AI system to assist embryo selection covering the in vitro fertilization cycle. With the multi-modal contrastive learning framework, this model shows superior performance in the prediction of morphology grading, euploidy status, and live-birth pote