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

Abstract

Demand for IVF treatment is growing, however success rates remain low partly due to the difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and can lead to significant inter-operator variability. Deep learning techniques could lead to improved embryo assessment and live birth prediction, however previous attempts neglect early developmental stages and often require vast amounts of data. Here, we demonstrate that even with limited data it is possible to train convolutional neural networks to classify developmental stage at high accuracies and predict live birth from various time-points throughout development. We identify key windows that are optimal for assessing embryo viability and demonstrate the importance of incorporating information from earlier stages. Our outcome predictor models are competitive with, and potentially outperform, human expert selection. The pipeline produced here could lead to an improved, standardised approach to embryo selection compatible with multiple transfer strategies.