Delineating the heterogeneity of preimplantation development via unsupervised clustering of embryo candidates for transfer using automated, accurate and standardized morphokinetic annotation

The majority of human embryos, whether naturally or in vitro fertilized (IVF), do not poses the capacity to implant within the uterus and reach live birth. Hence, selecting the embryos with the highest developmental potential to implant is imperative for improving pregnancy rates without prolonging time to pregnancy. The developmental potential of embryos can be assessed based on temporal profiling of the discrete morphokinetic events of preimplantation development. However, manual morphokinetic annotation introduces intra- and inter-observer variation and is time-consuming. Using a large clinically-labeled multicenter dataset of video recordings of preimplantation embryo development by time-lapse incubators, we trained a convolutional neural network and developed a classifier that performs fully automated, robust, and standardized annotation of the morphokinetic events with R-square 0.994 accuracy. To delineate the morphokinetic heterogeneity of preimplantation development, we performed unsupervised clustering of high-quality embryo candidates for transfer, which was independent of maternal age and blastulation rate. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters that are distinctively marked by poor synchronization of the third meiotic cell-cleavage cycle. We expect this work to advance the integration of morphokinetic-based decision support tools in IVF treatments and deepen our understanding of preimplantation heterogeneity.