A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: A retrospective model development and validation study
Published in The Lancet Digital Health, 2023
Citation: Barnes, J., Brendel, M., Gao, V. R., Rajendran, S., Kim, J., Li, Q., Malmsten, J. E., Sierra, J. T., Zisimopoulos, P., Sigaras, A., Khosravi, P., Meseguer, M., Zhan, Q., Rosenwaks, Z., Elemento, O., Zaninovic, N., & Hajirasouliha, I. (2023). A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. The Lancet. Digital health, 5(1), e28–e40. https://doi.org/10.1016/S2589-7500(22)00213-8
One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status.