Benchaib Mehdi, Labrune Elsa, Giscard d'Estaing Sandrine, Salle Bruno, Lornage Jacqueline
Hospices Civil de Lyon, HFME, Médecine de la Reproduction & Préservation de la Fertilité Féminine Bron cedex France.
UMR CNRS 5558 LBBE Villeurbanne Cedex France.
Reprod Med Biol. 2022 Sep 28;21(1):e12486. doi: 10.1002/rmb2.12486. eCollection 2022 Jan-Dec.
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).
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
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.
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.
本研究旨在利用延时技术（TLT），结合形态动力学参数和辅助生殖技术（ART）参数构建浅层神经网络（SNN），以辅助选择移植后最有可能实现活产（LB）的胚胎。
进行了一项回顾性观察单中心研究，纳入654个周期。使用K折交叉验证训练了三种SNN：多层感知器（MLP）、简单循环神经网络（简单RNN）和长短期记忆RNN（LSTM-RNN），以避免采样偏差。使用性能分数（如曲线下面积（AUC）、准确率、精确率、召回率和F1分数）来衡量SNN的预测能力。
在训练数据组中，MLP和简单RNN提供了最佳性能分数；然而，所有AUC均高于0.8。在验证数据组中，所有网络相当，性能分数无差异，且所有AUC值均高于0.8。
将形态动力学参数与ART参数相结合，可使SNN预测活产概率，根据性能分数，所有SNN似乎都有效。与这些SNN之一相结合的自动时间识别系统可实现选择移植囊胚的完全自动化。
