Looking into the future: a machine learning powered prediction model for oocyte return rates after cryopreservation.

Journal: Reproductive biomedicine online
PMID: 39642702

Abstract

RESEARCH QUESTION: Could a predictive model, using data from all US fertility clinics reporting to the Society for Assisted Reproductive Technology, estimate the likelihood of patients using their stored oocytes?

Authors

  • Yuval Fouks
    Boston IVF - IVIRMA Global Research Alliance, Waltham, MA, USA; Harvard T.H. Chan School of Public Health, Boston MA, USA; Reproductive Services Royal Women's Hospital, Melbourne Parkville, VIC; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. Electronic address: fouksy@thewomens.org.au.
  • Pietro Bortoletto
    Boston IVF - IVIRMA Global Research Alliance, Waltham, MA, USA; Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston MA, USA; Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston MA, USA.
  • Jeffrey Chang
    Harvard T.H. Chan School of Public Health, Boston MA, USA.
  • Alan Penzias
    Boston IVF - IVIRMA Global Research Alliance, Waltham, MA, USA; Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston MA, USA; Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston MA, USA.
  • Denis Vaughan
    Boston IVF - IVIRMA Global Research Alliance, Waltham, MA, USA; Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston MA, USA; Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston MA, USA.
  • Denny Sakkas
    Boston IVF - The Eugin Group, Waltham, Massachusetts.

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