  This work describes the development and validation of a fully automated deep
learning model, iDAScore v2.0, for the evaluation of embryos incubated for 2,
3, and 5 or more days. The model is trained and evaluated on an extensive and
diverse dataset including 181,428 embryos from 22 IVF clinics across the world.
For discriminating transferred embryos with known outcome (KID), we show AUCs
ranging from 0.621 to 0.708 depending on the day of transfer. Predictive
performance increased over time and showed a strong correlation with
morphokinetic parameters. The model has equivalent performance to KIDScore D3
on day 3 embryos while significantly surpassing the performance of KIDScore D5
v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences
without the need for user input, and provides a reliable method for ranking
embryos for likelihood to implant, at both cleavage and blastocyst stages. This
greatly improves embryo grading consistency and saves time compared to
traditional embryo evaluation methods.
