Retrospective Evaluation Blastocyst Development by Artificial Intelligency

Plain-language summary

In assisted reproductive technology (ART), selecting the most viable embryos from a large number of fertilized eggs is crucial. While techniques like morphological assessment, time-lapse monitoring systems, and pre-implantation genetic testing have improved the process, implantation success rates remain limited. The introduction of artificial intelligence in embryo evaluation, such as automated systems like EMA by AIVFTM, provides a promising alternative to enhance the accuracy and effectiveness of embryo selection. This study aims to assess the performance of the EMA system compared to traditional methods by examining its ability to rank embryos based on their potential for successful implantation, with the goal of increasing the chances of a successful pregnancy. This is the first clinical evaluation of this platform in France, offering new opportunities to improve decision-making in in-vitro fertilization.

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