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Accurate and Phenol Free DNA Sexing of Day 30 Porcine Embryos by PCR
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Deep learning-based early screening for porcine embryos with different developmental potential.

Yongjiang Yang1, Haoxing Li2, Dengfeng Bi3

  • 1State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.

Theriogenology
|March 22, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a deep learning model, MaxViT_T, to predict early-stage porcine embryo development for improved artificial reproduction technology (ART) efficiency. This AI tool enhances embryo screening by overcoming challenges posed by high lipid content in in vitro embryos.

Keywords:
Artificial intelligenceIn vitro cultureMaxViT_TPorcine embryos

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Area of Science:

  • Veterinary Medicine
  • Animal Reproduction
  • Bioinformatics

Background:

  • Porcine artificial reproduction technology (ART) efficiency is limited by the difficulty of predicting in vitro embryo developmental potential.
  • High cytoplasmic lipid content in embryos obscures optical clarity, hindering microscopic assessment and reliable prediction.

Purpose of the Study:

  • To develop and evaluate deep learning models for predicting the developmental potential of early-stage porcine embryos.
  • To establish a novel embryonic droplet culture system for collecting high-quality image data.

Main Methods:

  • Collected 10,041 bright-field images of porcine parthenogenetically activated (PA) embryos (1-cell to blastocyst stage).
  • Developed and benchmarked multiple deep learning models, including MaxViT_T, using curated developmental outcome annotations.
  • Validated model performance across different experimental batches with varying blastocyst formation rates.

Main Results:

  • Identified MaxViT_T as the most efficient deep learning model for predicting porcine embryo development.
  • MaxViT_T achieved peak prediction performance at the 4-cell stage.
  • The model demonstrated consistent prediction efficiency across diverse experimental conditions.

Conclusions:

  • The MaxViT_T model accurately predicts the developmental potential of early-stage porcine embryos.
  • This AI-driven approach offers a novel method for screening high-quality embryos in ART.
  • Improved embryo selection can enhance the overall efficiency of porcine reproductive technologies.