Development and validation of deep learning based embryo selection across multiple days of transfer
This work addresses embryo grading inconsistency and time inefficiency in IVF clinics by providing a reliable, automated tool for ranking embryos.
The researchers developed and validated a fully automated deep learning model, iDAScore v2.0, for embryo selection across multiple days, achieving AUCs from 0.621 to 0.708 for predicting implantation likelihood and outperforming existing methods on day 5+ embryos.
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.