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Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced Short...

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Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications.

Lucia Maresca1,2, Antonio D'Amato1, Camilla Coianiz1

  • 1IVIRMA Global Research Alliance, IVI Roma, 00161 Rome, Italy.

Bioengineering (Basel, Switzerland)
|June 26, 2026
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Summary

Artificial intelligence (AI) offers advanced embryo selection in in vitro fertilization, analyzing complex data beyond traditional methods. Further validation is needed for AI

Keywords:
artificial intelligencedeep learningembryo selectionin vitro fertilizationmachine learningmorphokineticstime-lapse imaging

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Area of Science:

  • Reproductive Medicine
  • Biotechnology
  • Medical Informatics

Background:

  • Embryo selection in in vitro fertilization (IVF) relies on subjective morphological assessment, limiting accuracy.
  • Assisted reproductive technologies (ART) have advanced, yet optimal embryo selection remains a challenge.
  • Integrating diverse clinical and biological data for embryo assessment is difficult with conventional methods.

Purpose of the Study:

  • To review current artificial intelligence (AI) approaches for embryo selection in IVF.
  • To discuss machine learning and deep learning techniques applied to embryo assessment.
  • To consider the clinical and ethical implications of AI in assisted reproduction.

Main Methods:

  • Review of AI models utilizing static embryo images and time-lapse imaging data.
  • Discussion of machine learning (ML) and deep learning (DL) techniques, including convolutional neural networks (CNNs) and spatiotemporal models.
  • Examination of AI model performance evaluation and clinical relevance of endpoints like live birth rate.

Main Results:

  • AI enables analysis of large datasets from embryo images, morphokinetics, and clinical variables.
  • AI models show potential for improved decision support in embryo selection.
  • Performance evaluation highlights the clinical relevance of AI-driven endpoints over traditional measures.

Conclusions:

  • AI presents a promising decision-support tool for enhancing embryo selection in IVF.
  • Further validation and standardized implementation are crucial for routine clinical adoption of AI.
  • Ethical considerations including transparency, responsibility, and regulation must be addressed for AI integration.