Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: a single-center large cohort retrospective study.

Journal: Fertility and sterility
PMID: 34246469

Abstract

OBJECTIVE: To analyze the performance of an annotation-free embryo scoring system on the basis of deep learning for pregnancy prediction after single vitrified blastocyst transfer (SVBT) compared with the performance of other blastocyst grading systems dependent on annotation or morphology scores.

Authors

  • Satoshi Ueno
    Kato Ladies Clinic, Tokyo, Japan.
  • Jørgen Berntsen
    Vitrolife A/S, Denmark.
  • Motoki Ito
    Kato Ladies Clinic, Tokyo, Japan.
  • Kazuo Uchiyama
    Kato Ladies Clinic, Tokyo, Japan.
  • Tadashi Okimura
    Kato Ladies Clinic, Tokyo, Japan.
  • Akiko Yabuuchi
    Kato Ladies Clinic, Tokyo, Japan.
  • Keiichi Kato
    Kato Ladies Clinic, Tokyo, Japan. Electronic address: k-kato@towako.net.

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