BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

Journal: Human reproduction (Oxford, England)
PMID: 38396213

Abstract

STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?

Authors

  • Helen Y Yang
    Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
  • Brian D Leahy
    Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
  • Won-Dong Jang
  • Donglai Wei
    School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
  • Yael Kalma
    Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Roni Rahav
    Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Ariella Carmon
    Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Rotem Kopel
    Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Foad Azem
    Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Marta Venturas
    Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
  • Colm P Kelleher
    Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
  • Liz Cam
    Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
  • Hanspeter Pfister
  • Daniel J Needleman
    Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
  • Dalit Ben-Yosef
    Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.

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