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Related Concept Videos

In Vitro Fertilization01:24

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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...
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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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Using Mouse Oocytes to Assess Human Gene Function During Meiosis I
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Artificial intelligence for optimal in vitro fertilization morphokinetics.

Emily Frisch1, Anant Jain2, Chanel Fischetti3

  • 1Obstetrics and Gynecology and Women's Health Institute, Cleveland Clinic, Cleveland, OH, United States.

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|May 21, 2025
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Summary
This summary is machine-generated.

This study developed an artificial intelligence model using deep learning to classify human embryonic phases from time-lapse imaging videos, achieving high accuracy. This AI tool can optimize embryo selection for improved in vitro fertilization outcomes.

Keywords:
Artificial intelligenceEmbryo morphokineticsIn vitro fertilization

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence
  • Embryology

Background:

  • Time-lapse imaging (TLI) provides continuous monitoring of human embryo development.
  • Accurate assessment of embryonic morphokinetic phases is crucial for successful in vitro fertilization (IVF).
  • Manual annotation of TLI videos is time-consuming and resource-intensive.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI) model for automated classification of human embryonic morphokinetic phases using TLI videos.
  • To leverage deep learning, specifically EfficientNetB4, for enhanced image classification of embryo development.

Main Methods:

  • Utilized a publicly available IVF dataset comprising 704 TLI videos (2.4 million images) from 716 couples.
  • Employed convolutional neural networks (CNNs) and the EfficientNetB4 architecture for model training.
  • Evaluated model performance using accuracy, specificity, Matthews correlation coefficient, and AUC.

Main Results:

  • The AI model achieved an overall accuracy of 0.71, sensitivity of 0.59, and specificity of 0.98.
  • Demonstrated a high Multiclass Receiver Operating Characteristic area under the curve (AUC) score of 0.96.
  • The model effectively differentiates and classifies human embryonic phases from TLI data.

Conclusions:

  • Convolutional neural network models are highly effective for classifying human embryonic phases from TLI.
  • AI offers a promising approach to select ideal embryo developmental stages, moving beyond chronological age.
  • AI can optimize the processing of large-scale embryo imaging data, aiding embryologists and improving IVF success rates.