An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology

Abstract

Embryo quality is a critical determinant of clinical outcomes in assisted reproductive technology (ART). A recent clinical trial investigating preimplantation DNA methylation screening (PIMS) revealed that whole genome DNA methylation level is a novel biomarker for assessing ART embryo quality. Here, we reinforced and estimated the clinical efficacy of PIMS. We introduce PIMS-AI, an innovative artificial intelligence (AI) based model, to predict the probability of an embryo producing live birth and subsequently assist ART embryo selection. Our model demonstrated robust performance, achieving an area under the curve (AUC) of 0.90 in cross-validation and 0.80 in independent testing. In simulated embryo selection, PIMS-AI attained an accuracy of 81% in identifying viable embryos for patients. Notably, PIMS-AI offers significant advantages over conventional preimplantation genetic testing for aneuploidy (PGT-A), including enhanced embryo discriminability and the potential to benefit a broader patient population. In conclusion, our approach holds substantial promise for clinical application and has the potential to significantly improve the ART success rate.

Graphical abstract

Keywords

Assisted reproductive technology / DNA methylation / Artificial intelligence / Embryo selection

Acknowledgements

This work was supported under grants from the National Natural Science Foundation of China (22006103) and Key-Area Research and Development Program of Guangdong Province (2023B1111020006).

RIGHTS & PERMISSIONS

2023 The Author(s)