Artificial intelligence-powered assisted ranking of sibling embryos to increase first cycle pregnancy rate

• Published in: Reproductive biomedicine online Vol. 49; no. 1; p. 103887
• Main Authors: Dissler, Nina, Nogueira, Daniela, Keppi, Bertrand, Sanguinet, Pierre, Ozanon, Christophe, Geoffroy-Siraudin, Cendrine, Pollet-Villard, Xavier, Boussommier-Calleja, Alexandra
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.07.2024
• Subjects: Adult; Artificial Intelligence; birth; embryo evaluation; Embryo Transfer - methods; Female; Fertilization in Vitro - methods; Humans; Pregnancy; Pregnancy Rate; Siblings; time to pregnancy; time-lapse incubator systems; time to pregnancy; time-lapse incubator systems; birth; artificial intelligence; embryo evaluation; pregnancy

Could EMBRYOLY, an artificial intelligence embryo evaluation tool, assist embryologists to increase first cycle pregnancy rate and reduce cycles to pregnancy for patients? Data from 11,988 embryos were collected via EMBRYOLY from 2666 egg retrievals (2019–2022) across 11 centres in France, Spain and...