Viability Assessment of Bovine Embryos: A Public Dataset and Deep Learning Baselines

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Original abstract

Improving the success rates of cattle breeding is essential for sustainable agriculture, global food security, and high-quality livestock production. Currently, determining whether a lab-grown bovine embryo is healthy enough for a successful pregnancy requires highly trained experts to manually evaluate days of continuous time-lapse video footage. This process is not only incredibly time-consuming but also highly subjective; human reviewers often suffer from visual fatigue when tracking subtle, microscopic cellular changes over a seven-day period, leading to significant disagreement among even top experts on an embryo’s true potential. Furthermore, assessing bovine embryos is notoriously difficult due to their dark, lipid-dense cellular structures and the complex developmental differences between standard in vitro fertilization and advanced cloning techniques. To accelerate solutions to these challenges, this research introduces the first publicly available collection of bovine embryo development videos, breaking down the data-privacy barriers that have previously prevented global scientists from developing automated tools in this agricultural sector. We employ computer vision and deep learning to create an automated pipeline for bovine embryo fate determination. Our system can seamlessly analyze a full seven days of complex embryo growth in a matter of seconds. Results show that this automated approach achieves diagnostic accuracy comparable to that of human experts, providing a consistent, fatigue-free “second opinion” for embryologists. By automating this highly complex visual task, we can standardize embryo evaluation across different laboratories, drastically reduce the workload of agricultural specialists, and help farmers make more reliable, cost-effective decisions, ultimately driving more efficient and productive livestock breeding strategies worldwide.

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