论文标题
使用IVF延时成像的胚胎植入概率的数据驱动预测
Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging
论文作者
论文摘要
为了帮助那些不孕症患者的人体外的人卵受精的过程称为体外受精（IVF）。尽管是辅助生殖技术（ART）的最有效方法，但IVF的平均成功率仅为20-40％。对于程序的成功至关重要的一个步骤是选择要转移到患者的胚胎，这是一个通常手动进行的过程，而没有任何普遍接受和标准化的标准。在本文中，我们描述了一种新型的数据驱动系统，该系统训练有素，可以直接预测胚胎发生延时成像视频的胚胎植入概率。使用回顾性收集的272个胚胎的视频，我们证明，与胚胎学家外部面板相比，我们的算法会导致阳性预测值增加12％，而负预测价值增加了29％。
The process of fertilizing a human egg outside the body in order to help those suffering from infertility to conceive is known as in vitro fertilization (IVF). Despite being the most effective method of assisted reproductive technology (ART), the average success rate of IVF is a mere 20-40%. One step that is critical to the success of the procedure is selecting which embryo to transfer to the patient, a process typically conducted manually and without any universally accepted and standardized criteria. In this paper we describe a novel data-driven system trained to directly predict embryo implantation probability from embryogenesis time-lapse imaging videos. Using retrospectively collected videos from 272 embryos, we demonstrate that, when compared to an external panel of embryologists, our algorithm results in a 12% increase of positive predictive value and a 29% increase of negative predictive value.
