Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Research Division, AIVF Ltd., Tel Aviv, 69271, Israel.
Adv Sci (Weinh). 2023 Sep;10(27):e2207711. doi: 10.1002/advs.202207711. Epub 2023 Jul 28.
High-content time-lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, whether, and to what extent the information encoded within "sibling" embryos from the same IVF cohort contributes to the performance of machine learning-based implantation prediction is explored. First, it is shown that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, it is demonstrated that this unlabeled data boosts implantation prediction performance. Third, the cohort properties driving embryo prediction, especially those that rescued erroneous predictions, are characterized. The results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing the inherent noise of the individual transferred embryo.
基于机器学习的高通量延时胚胎成像正在彻底改变体外受精（IVF）领域。然而，目前旨在预测胚胎着床的研究中，绝大多数 IVF 胚胎都没有被移植到子宫内，这些具有未知着床结果的大量胚胎被忽略了。在这里，我们探讨了来自同一 IVF 队列的“同胞”胚胎中编码的信息是否以及在何种程度上有助于基于机器学习的着床预测。首先，研究表明胚胎着床与从队列同胞中提取的属性相关。其次，研究结果表明，这些未标记数据可以提高着床预测性能。第三，研究还描述了推动胚胎预测的队列特性，特别是那些纠正错误预测的特性。研究结果表明，胚胎着床的预测模型可以通过减少单个移植胚胎的固有噪声，从被忽视的、广泛可用的同胞胚胎未标记数据中获益。
