A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.

Journal: Fertility and sterility
PMID: 34256948

Abstract

OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.

Authors

  • Eduardo Hariton
    Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States.
  • Ethan A Chi
    Department of Artificial Intelligence, Stanford University, Palo Alto, California.
  • Gordon Chi
    Department of Computer Science, Stanford University, Stanford, California.
  • Jerrine R Morris
    Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California.
  • Jon Braatz
    Department of Artificial Intelligence, Stanford University, Palo Alto, California.
  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • Mitchell Rosen
    Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California.

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