From Pixels to Pregnancies: AI-Driven Oocyte Grading for Scalable Livestock Breeding

Sustainable livestock breeding is essential to meeting the food demands of a growing global population. Assisted reproductive technologies (ART), such as in vitro fertilization(IVF), are increasingly used to enhance reproductive efficiency. A key determinant of IVF success is the quality of the oocyte, which directly affects fertilization, embryo development, and blastocyst yield. However, oocyte grading today remains a subjective and inconsistent process, creating variability that affects the entire in vitro embryo production pipeline. We introduce a deep learning framework for automated oocyte grading, built on the first dataset of its kind: 1,140 bovine cumulus–oocyte complex (COC) images labeled according to the International Embryo Transfer Society (IETS) scale. Our models achieve up to 65\% accuracy across four grades, improving to over 80\% when grouped into industry relevant quality categories. By aligning with IETS guidelines, we establish the first benchmark for standardized oocyte grading in livestock IVF. This work provides a strong foundation for AI-based assisted livestock breeding, offering consistency, reduced human variability, and increased throughput in livestock breeding.