Machine learning embryo evaluation reliably predicts embryo stage and morphokinetic activity

The utilization of in vitro fertilization (IVF) and embryo trans­fer (ET) to accelerate genetic selection of both beef and dairy cattle has experienced a rapid increase of adoption in recent years. However, the industry grapples with a pronounced shortage of skilled practitioner’s adept in these specialized procedures, especially laboratory skills including embryo evaluation. The ability to proficiently evaluate embryo qual­ity is arguably one of the most challenging components of IVF and ET and proper selection of healthy embryos for transfer is critical to the success of the ET/IVF procedure. Recent advance­ments in computerized systems and machine learning (ML) can standardize and automate this evaluation process, oftentimes outperforming the morphological assessment of the practitio­ner. Practical concerns of utilizing ML/CV to evaluate embryos includes the variation of embryo appearance which can change based on their orientation in the culture dish. Therefore, objec­tives of this study aim to test the reproducibility of computer­ized systems and ML results by repeatedly evaluating embryos oriented in changing positions.