Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning.

Journal: Journal of assisted reproduction and genetics
PMID: 37423932

Abstract

PURPOSE: This article aims to assess how differences in maternal age distributions between IVF clinics affect the performance of an artificial intelligence model for embryo viability prediction and proposes a method to account for such differences.

Authors

  • Martin N Johansen
    Vitrolife A/S, Jens Juuls Vej 18-20, 8260, Viby J, Denmark. mnjohansen@vitrolife.com.
  • Erik T Parner
    Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark.
  • Mikkel F Kragh
    Deparment of Engineering, Aarhus University, Denmark; Vitrolife A/S, Denmark. Electronic address: mkha@eng.au.dk.
  • Keiichi Kato
    Kato Ladies Clinic, Tokyo, Japan. Electronic address: k-kato@towako.net.
  • Satoshi Ueno
    Kato Ladies Clinic, Tokyo, Japan.
  • Stefan Palm
    MVZ PAN Institut, Cologne, Germany.
  • Manuel Kernbach
    MVZ PAN Institut, Cologne, Germany.
  • Basak Balaban
    American Hospital of Istanbul, In vitro fertilization lab, Istanbul, Turkey.
  • Ipek Keles
    Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Koc University School of Medicine, Istanbul, Turkey.
  • Anette V Gabrielsen
    Fertility Clinic, Horsens Regional Hospital, Horsens, Denmark.
  • Lea H Iversen
    Fertility Clinic, Horsens Regional Hospital, Horsens, Denmark.
  • Jørgen Berntsen
    Vitrolife A/S, Denmark.

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