7/6/2023 0 Comments Time lapse tool 2.3![]() ![]() Thus, new information is available to embryologists regarding the choice of the embryo(s) to be transferred, that is morphokinetic data and embryos images at different stages of their growth. In order to improve the embryos quality, incubators can be equipped with a time-lapse technology (TLT) that enables the culture conditions improvement and the embryo growth longitudinal follow-up. ![]() The choice of embryo to be transferred is based on either morphological criteria at D2 (day 2 post fertilization), 9 at D3, 10 or at the blastocyst stage, 11 or on preimplantation genetic diagnosis (PGD) techniques. All aspects of the artificial reproductive technology (ART) process have benefited from this effort: ovarian hyper-stimulation protocols, 2 embryo culture conditions, 3, 4 embryo transfer procedures, 5, 6 and embryo freezing techniques 7, 8 have been improved. Since the first child issued from in vitro fertilization (IVF) was born in 1978 1 physicians have never stopped trying to improve people's chances of getting a child. An automatic time recognition system coupled to one of these SNNs could allow a complete automation to choose the blastocyst(s) to be transferred. ConclusionĬoupling 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. In the validating data group, all networks were equivalent with no performance scores difference and all AUC values were above 0.8. In the training data group, MLP and simple RNN provide the best performance scores however, all AUCs were above 0.8. ![]() The predictive power of SNNs was measured using performance scores as AUC (area under curve), accuracy, precision, Recall and F1 score. 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. MethodsĪ retrospective observational single-center study was performed, 654 cycles were included. 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).
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