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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

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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.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...
481

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Related Experiment Video

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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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Pseudo contrastive labeling for predicting IVF embryo developmental potential.

I Erlich1,2, A Ben-Meir3,4, I Har-Vardi3,5

  • 1The Alexender Grass Center for Bioengineering, School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel. ityer82@gmail.com.

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Summary

Identifying high-potential embryos is crucial for in vitro fertilization success. This study introduces a novel time-conditioned classifier that significantly improves embryo selection accuracy, outperforming existing methods and expert assessments.

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Area of Science:

  • Embryology
  • Reproductive Medicine
  • Artificial Intelligence in Medicine

Background:

  • In vitro fertilization (IVF) success is limited by high failure rates per embryo transfer.
  • Current embryo assessment methods lack predictive power and are time-specific.
  • Existing automated methods struggle with data variability and temporal dynamics.

Purpose of the Study:

  • To develop a novel, time-conditioned automated method for accurately identifying embryos with high developmental potential.
  • To improve embryo selection accuracy in IVF by integrating temporal developmental data.
  • To enhance the predictive power of embryo assessment beyond current standards.

Main Methods:

  • A novel classifier was developed, conditioned on the time elapsed since fertilization.
  • The classifier was integrated over time, generating soft labels for sample pairs.
  • A new classifier was trained on these soft labels, improving accuracy and predictive power.

Main Results:

  • The proposed method demonstrated significant accuracy improvements as early as 30 hours post-fertilization.
  • Integrating classification scores further enhanced the predictive capabilities of the model.
  • The method outperformed established systems like KIDScore-D3 and expert evaluations.

Conclusions:

  • The developed time-conditioned classifier offers a superior approach to identifying viable embryos in IVF.
  • This automated method provides enhanced accuracy and predictive power for embryo selection.
  • The findings suggest a significant advancement in assisted reproductive technologies through AI-driven embryo assessment.