A SWOT Analysis of Human- And Machine Learning- Based Embryo Assessment

Abstract

Embryo assessment is the most critical step of an IVF cycle. Morphologic evaluation of embryonic development has been the conventional method of embryo selection for transfer. However, this evaluation is subjective due to the diversity of skills and experience of embryologists. Machine learning (ML)-based models have been implemented in many clinical applications and considered a promising solution for efficient and unbiased performance. Therefore, this paper performs the SWOT analysis on human- and ML-based embryo assessments, representing conventional and artificial intelligence-assisted medicines, respectively. After analyzing various perspectives of each approach, the appropriate strategies are proposed for gradually shifting from the conventional to the ML-based approach in embryo evaluation.