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Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.

Jorge Ten1, Leyre Herrero2, Ángel Linares3

  • 1Instituto Bernabéu Alicante, Avda. Albufereta, 31, 03016, Alicante, Spain. jten@institutobernabeu.com.

Reproductive Biology and Endocrinology : RB&E
|September 11, 2024
PubMed
Summary

Machine learning models like Random Forest and AdaBoost can predict embryo implantation and live birth success in IVF. Key embryo development timings, such as hatching, significantly influence these outcomes, improving assisted reproduction treatments.

Keywords:
Artificial intelligenceEmbryo morphokineticHatching blastocystImplantation and live birthMachine learning

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Area of Science:

  • Reproductive Medicine
  • Data Science in Healthcare
  • Embryology

Background:

  • Artificial intelligence and data science are increasingly utilized in assisted reproduction.
  • Time-lapse technology incubators provide valuable data for embryo assessment.
  • Improving in vitro fertilization (IVF) clinical outcomes is a key goal.

Purpose of the Study:

  • To compare and identify the most predictive machine learning algorithms for embryo implantation.
  • To analyze morphokinetic and morphological variables using a known implantation database.
  • To recognize the most predictive embryo parameters for enhancing IVF treatment success.

Main Methods:

  • A multicenter retrospective cohort study involving 378 egg donor recipients undergoing fresh single embryo transfer.
  • Embryos were cultured in Geri® time-lapse incubators and analyzed for morphokinetic events.
  • Ten machine learning algorithms, including Random Forest and AdaBoost, were applied and optimized.

Main Results:

  • Random Forest showed the highest predictive power for implantation (AUC=0.725).
  • AdaBoost classification trees were most predictive for live birth (AUC=0.749).
  • Time to hatching and other developmental timings (pronuclei, cleavage, compaction) were significant predictors.

Conclusions:

  • Random Forest and AdaBoost are effective machine learning models for predicting implantation and live birth in egg donation programs.
  • Time to blastocyst hatching is a crucial parameter for predictive models.
  • Embryonic developmental processes like syngamy, genomic imprinting, and compaction are vital for successful implantation and live birth.