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In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
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Artificial intelligence-assisted selective modified natural frozen embryo transfer.

Michal Youngster1, Nevo Itzhak2, Eden Moran2

  • 1IVF Unit, Department of Obstetrics and Gynecology, Shamir Medical Center, Zerifin, Israel.; Faculty of Medical and Health Sciences, Tel Aviv University, Tel-Aviv, Israel..

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Summary

An artificial intelligence algorithm can help avoid frozen embryo transfers on clinic closure days by selectively triggering ovulation. This AI tool minimizes transfers on non-working days while preserving the natural cycle benefits for most patients.

Keywords:
Artificial intelligenceFrozen embryo transferIVFMachine learningModified natural cycle

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Area of Science:

  • Reproductive Endocrinology
  • Artificial Intelligence in Medicine
  • In Vitro Fertilization

Background:

  • Natural-cycle frozen embryo transfer (NC-FET) is a common IVF procedure.
  • Scheduling FETs on clinic non-working days presents logistical challenges.
  • Optimizing FET scheduling is crucial for patient convenience and clinic efficiency.

Purpose of the Study:

  • To evaluate an artificial intelligence (AI) algorithm for selective triggering in NC-FET.
  • To determine if the AI can prevent embryo transfers on undesired clinic closure days.
  • To assess the impact of AI-guided triggering on cycle flexibility and natural cycle preservation.

Main Methods:

  • Retrospective cohort study of 4029 FET cycles.
  • Utilized an AI algorithm incorporating an ovulation prediction model (LH, estradiol, progesterone, follicle size).
  • Algorithm suggested triggering only when natural ovulation predicted a transfer on a non-working day.

Main Results:

  • The AI algorithm identified ~40% of cycles requiring intervention to avoid non-working day transfers.
  • Ovulation shifts were 2 days or less in 89% of intervened cycles.
  • The algorithm accurately predicted natural ovulation in 96% of non-intervention cycles.

Conclusions:

  • A selective modified NC-FET algorithm can minimize transfers on non-working days.
  • The AI approach allows flexibility while maintaining natural cycle advantages.
  • This AI tool can enhance NC-FET practice efficiency and convenience.