Deep Learning for Human Embryo Classification at the Cleavage Stage (day 3).

To date, deep learning has assisted in classifying embryos as early as day 5 after insemination. We investigated whether deep neural networks could successfully predict the destiny of each embryo (discard or transfer) at an even earlier stage, namely at day 3. We first assessed whether the destiny of each embryo could be derived from technician scores, using a simple regression model. We then explored whether a deep neural network could make accurate predictions using images alone. We found that a simple 8-layer network was able to achieve 75.24% accuracy of destiny prediction, outperforming deeper, state-of-the-art models that reached 68.48% when applied to our middle slice images. Increasing focal points from a single (middle slice) to three slices per image did not improve accuracy. Instead, accounting for the “batch effect”, that is, predicting an embryo’s destiny in relation to other embryos from the same batch, greatly improved accuracy, to a level of 84.69% for unseen cases. Importantly, when analyzing cases of transferred embryos, we found that our lean, deep neural network predictions were correlated (0.65) with clinical outcomes.