RESUMEN
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.
RESUMEN
Plants in Ainsliaea genus, belongs to Compositae family, are traditional Chinese medicine and widely used in folk. These plants contain various types of chemical components, and main components are sesquiterpene lactone and its glycosides. In addition, there are triterpenoids, flavonoids, steroids, phenolic acid, long chain fatty acid and volatile oils. Recently, much attention has been payed to varlous research of A. fragrans. This paper reviewed and summarized the chemical components to provide the theoretical basis for the use of Ainsliaea.