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<p>The assessment of oocyte and blastocyst quality plays a pivotal role in reproductive biology, directly influencing the success of assisted reproductive technologies (ART) in both humans and farm animals. In livestock, technologies such as Ovum Pick-Up and In Vitro Embryo Production (OPU-IVEP) have revolutionized genetic improvement strategies by enabling the production of a higher number of genetically superior offspring from elite females. However, the manual evaluation of oocytes and embryos remains subjective, time-consuming, and susceptible to human error. Recent advances in Artificial Intelligence (AI), particularly in computer vision and deep learning, have opened new avenues for automating the assessment process. AI models such as convolutional neural networks (CNNs) have demonstrated high accuracy in classifying oocyte and embryo quality, providing standardized, rapid, and reproducible evaluations. This review focuses on the applications of artificial intelligence in bovine oocyte and blastocyst grading, highlighting its potential to improve assessment accuracy, support OPU–IVEP programs, and enhance reproductive efficiency.</p>

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Applications of artificial intelligence in bovine reproductive assessment: focus on oocytes and blastocysts

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Bharati Pandey,
  • Rutuja Shelke,
  • Gaurav Tripathi,
  • Manoj K. Singh,
  • Naresh L. Selokar

摘要

The assessment of oocyte and blastocyst quality plays a pivotal role in reproductive biology, directly influencing the success of assisted reproductive technologies (ART) in both humans and farm animals. In livestock, technologies such as Ovum Pick-Up and In Vitro Embryo Production (OPU-IVEP) have revolutionized genetic improvement strategies by enabling the production of a higher number of genetically superior offspring from elite females. However, the manual evaluation of oocytes and embryos remains subjective, time-consuming, and susceptible to human error. Recent advances in Artificial Intelligence (AI), particularly in computer vision and deep learning, have opened new avenues for automating the assessment process. AI models such as convolutional neural networks (CNNs) have demonstrated high accuracy in classifying oocyte and embryo quality, providing standardized, rapid, and reproducible evaluations. This review focuses on the applications of artificial intelligence in bovine oocyte and blastocyst grading, highlighting its potential to improve assessment accuracy, support OPU–IVEP programs, and enhance reproductive efficiency.