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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

237
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...
237

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Optimizing trigger timing in minimal ovarian stimulation for In Vitro fertilization using machine learning models

Nayeli Areli Pérez-Padilla1, Rodolfo Garcia-Sanchez2, Omar Avalos1

  • 1Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico.

Computers in Biology and Medicine
|July 25, 2024
PubMed
Summary

Optimizing trigger shot timing in In Vitro Fertilization (IVF) is crucial. An AI model can predict outcomes, potentially increasing usable blastocyst production by 46% in minimal stimulation cycles.

Keywords:
Artificial intelligenceArtificial neural networksHyperparameter tuningIn vitro fertilizationMachine learningMinimal ovarian stimulationMulti-layer perceptronTrigger timingUseable blastocyst

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Biostatistics

Background:

  • Optimal trigger timing (TT) is vital for oocyte maturation and release in In Vitro Fertilization (IVF).
  • Determining precise TT is challenging due to multiple variables, impacting IVF success rates, especially in minimal ovarian stimulation protocols.

Purpose of the Study:

  • To develop a machine learning multi-output model for predicting IVF outcomes (retrieved oocytes, mature oocytes, fertilized oocytes, usable blastocysts).
  • To enhance trigger shot timing (TT) by identifying patients with potentially suboptimal timing in minimal stimulation cycles.

Main Methods:

  • Development of a machine learning multi-output predictive model.
  • Utilizing the model to analyze trigger shot timing in minimal ovarian stimulation cycles.
  • Statistical validation of the model's accuracy and performance.

Main Results:

  • Approximately 27% of treatments utilized a suboptimal trigger shot day.
  • Optimizing TT with the AI model showed a potential 46% increase in usable blastocyst production.
  • The model accurately predicted IVF outcomes within a 48-hour window post-trigger shot.

Conclusions:

  • AI-driven predictive models can serve as valuable tools for optimizing trigger shot timing in IVF.
  • Enhancing TT through AI can improve IVF success rates, particularly in minimal ovarian stimulation.
  • The developed AI model contributes to making the IVF process safer and more efficient.