The 'golden fleece of embryology' eludes us once again: a recent RCT using artificial intelligence reveals again that blastocyst morphology remains the standard to beat.

Abstract

Grading of blastocyst morphology is used routinely for embryo selection with good outcomes. A lot of effort has been placed in IVF to search for the prize of selecting the most viable embryo to transfer ('the golden fleece of embryology'). To improve on morphology alone, artificial intelligence (AI) has also become a tool of interest, with many retrospective studies being published with impressive prediction capabilities. Subsequently, AI has again raised expectations that this 'golden fleece of embryology' was once again within reach. A recent RCT however was not able to demonstrate non-inferiority using a deep learning algorithm 'iDAScore version 1' for clinical pregnancy rate when compared to standard morphology. Good blastocyst morphology has again proven itself as a high bar in predicting live birth. We should however not give up on the development of further approaches which may allow us to identify extra features of viable embryos that are not captured by morphology.