Deep learning pipeline reveals key moments in human embryonic development predictive of live birth in IVF

Demand for IVF treatment is growing, however success rates remain low partly due to difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and may not take advantage of the most informative moments in embryo development. Here, we apply convolutional neural networks (CNNs) to identify key windows in preimplantation human development that can be linked to embryo viability and are therefore suitable for the early grading of IVF embryos. We show how machine learning models trained at these developmental time-points can be used to refine overall embryo viability assessment. Exploiting the well-known capabilities of transfer learning, we illustrate the performance of CNN models for very limited data sets, paving the way for the use on a clinic-by-clinic basis, catering for local data heterogeneity.