Machine learning embryo evaluation reliably predicts embryo stage and morphokinetic activity
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: C. Wells, M. Rea, C. Hayden, R. Killingsworth
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4408882996