A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.

Journal: The Lancet. Digital health
Published Date: Jan 1, 2023

Abstract

BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information.

Authors

  • Josue Barnes
    Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
  • Matthew Brendel
    Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY USA.
  • Vianne R Gao
    Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA.
  • Suraj Rajendran
    Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA.
  • Junbum Kim
    Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
  • Qianzi Li
    Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
  • Jonas E Malmsten
    Weill Cornell Medicine, New York, New York.
  • Jose T Sierra
    QED Analytics, Princeton, NJ, USA.
  • Pantelis Zisimopoulos
    Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
  • Alexandros Sigaras
    Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.
  • Pegah Khosravi
    Institute for Computational Biomedicine, Weill Cornell Medical College, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
  • Marcos Meseguer
    Instituto Valenciano de Infertilidad (IVI) Valencia, INCLIVA-Universidad de Valencia, Valencia, Spain.
  • Qiansheng Zhan
    Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Zev Rosenwaks
    Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, New York, New York.
  • Olivier Elemento
    Institute for Precision Medicine.
  • Nikica Zaninovic
    Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA.
  • Iman Hajirasouliha
    Institute for Computational Biomedicine, Weill Cornell Medical College, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medical College, NY, USA; The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.

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