Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5. We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokinetic variables related to women and embryos were recorded and combined. Some embryos reached the expanded blastocyst stage on day 5 (BL Group, n = 210), some others did not (nBL Group, n = 365). The novel EmbryoMLSelection framework was developed following four-steps: Feature Selection, Rules Extraction, Rules Selection and Rules Evaluation. Six rules composed by a combination of 8 variables were finally selected, and provided a predictive power described by an AUC of 0.84 and an accuracy of 81%. We provided herein a new feature-signature able to identify with an high performance embryos with the best developmental competence to reach the expanded blastocyst stage on day 5. Clear and clinically relevant cut-offs were identified for each considered variable, providing an objective tool for early embryo developmental assessment.

中文翻译：

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基于早期胚胎形态动力学的新型机器学习框架识别与囊胚发育相关的特征特征

人工智能需要将计算机算法应用于延时技术产生的大量异构形态动力学数据。在此背景下，开发了机器学习（ML）方法，以帮助胚胎学家利用自动化和客观的预测模型来标准化人类胚胎评估。在这项研究中，我们旨在开发一种基于 ML 的新型策略，以确定与第 5 天囊胚发育阶段预测相关的相关模式。我们回顾性分析了从我们科室接受 IVF 的 80 名女性获得的 575 个胚胎的形态动力学。使用 Geri plus® 延时系统记录胚胎形态动力学。总体而言，记录并组合了 30 个与女性和胚胎相关的临床、形态学和形态动力学变量。一些胚胎在第 5 天达到扩大的囊胚阶段（BL 组，n = 210），另一些则没有（nBL 组，n = 365）。新颖的 EmbryoMLSelection 框架的开发分为四个步骤：特征选择、规则提取、规则选择和规则评估。最终选择了由8个变量组合组成的6个规则，并提供了AUC为0.84的预测能力和81%的准确率。我们在此提供了一个新的特征签名，能够识别具有最佳发育能力的高性能胚胎，以在第 5 天达到扩大的囊胚阶段。为每个考虑的变量确定了明确且临床相关的截止值，为早期胚胎发育评估。