Assaf Zaritsky

Automated live embryo imaging has transformed in vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Herein, it is established that this strategy can lead to suboptimal selection of embryos. It is revealed that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, it is found that ambiguous labels of failed implantations, due to either low-quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, conceptual and practical steps are proposed to enhance machine learning-driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking and reducing label ambiguity.