ReviewJournal of assisted reproduction and genetics2026
Applications of artificial intelligence in bovine reproductive assessment: focus on oocytes and blastocysts.
Review in Journal of assisted reproduction and genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.