Shallow artificial networks with morphokinetic time-lapse parameters coupled to ART data allow to predict live birth

Purpose The purpose of this work was to construct shallow neural networks (SNN) using time-lapse technology (TLT) from morphokinetic parameters coupled to assisted reproductive technology (ART) parameters in order to assist the choice of embryo(s) to be transferred with the highest probability of achieving a live birth (LB). Methods A retrospective observational single-center study was performed, 654 cycles were included. Three SNN: multilayers perceptron (MLP), simple recurrent neuronal network (simple RNN) and long short term memory RNN (LSTM-RNN) were trained with K-fold cross-validation to avoid sampling bias. The predictive power of SNNs was measured using performance scores as AUC (area under curve), accuracy, precision, Recall and F1 score. Results In the training data group, MLP and simple RNN provide the best performance scores; however, all AUCs were above 0.8. In the validating data group, all networks were equivalent with no performance scores difference and all AUC values were above 0.8. Conclusion Coupling morphokinetic parameters with ART parameters allows to SNNs to predict the probability of LB, and all SNNs seems to be efficient according to the performance scores. An automatic time recognition system coupled to one of these SNNs could allow a complete automation to choose the blastocyst(s) to be transferred.

Automated summary

This study aimed to construct shallow neural networks using time-lapse technology and morphokinetic parameters coupled to assisted reproductive technology parameters. The predictive power of the networks was measured using performance scores, with all networks showing efficiency in predicting the probability of achieving a live birth.