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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...
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Embryo selection with artificial intelligence: how to evaluate and compare methods?

Mikkel Fly Kragh1,2, Henrik Karstoft3

  • 1Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark. mikkelkragh@gmail.com.

Journal of Assisted Reproduction and Genetics
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Summary

Artificial intelligence (AI) in embryo selection for in vitro fertilization (IVF) shows promise but faces challenges. Studies often use different metrics for evaluating AI models, hindering direct comparisons and potentially introducing bias.

Keywords:
Artificial intelligenceEmbryo selectionModel evaluation and comparisonSelection bias

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence
  • Embryology

Background:

  • In vitro fertilization (IVF) utilizes embryo selection to identify the best embryos for transfer or cryopreservation.
  • Artificial intelligence (AI) is increasingly employed to automate and enhance embryo selection from microscopy images.
  • Current AI models are evaluated based on their ability to rank embryos or predict pregnancy outcomes.

Purpose of the Study:

  • To provide a technical overview of current AI models in embryo selection.
  • To clarify the distinctions between AI model evaluation for ranking versus pregnancy prediction.
  • To address the challenges in comparing AI models across studies and highlight potential biases.

Main Methods:

  • Technical analysis of AI model training and evaluation methodologies in embryo selection.
  • Discussion of common performance metrics and their relation to ranking and prediction objectives.
  • Examination of bias in retrospective cohort studies comparing AI to manual evaluation.

Main Results:

  • Divergent evaluation objectives (ranking vs. prediction) lead to varied reporting of AI model performance metrics.
  • Comparisons across studies are often confounded by differing outcomes and data.
  • Retrospective studies comparing AI to manual selection are prone to inevitable selection bias.

Conclusions:

  • Standardizing evaluation metrics and methodologies is crucial for reliable AI model assessment in IVF.
  • Careful consideration of potential biases is necessary when interpreting AI performance, especially in retrospective analyses.
  • Further research is needed to establish robust frameworks for AI development and validation in clinical embryology.