基于数据驱动的IVF延时成像预测胚胎着床概率
Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging
摘要：
将人类卵子移植到体外以帮助那些患有不孕症的人怀孕的过程被称为体外受精（IVF）。尽管IVF是辅助生殖技术（ART）中最有效的方法，但其平均成功率仅为20- 40%。对手术成功至关重要的一个步骤是选择将哪个胚胎移植给患者，这是一个通常手动进行的过程，没有任何普遍接受和标准化的标准。在本文中，我们描述了一种新的数据驱动的系统训练，直接预测胚胎植入的概率从胚胎发生的时间推移成像视频。使用从272个胚胎中回顾性收集的视频，我们证明，与外部胚胎学家小组相比，我们的算法导致阳性预测值增加12%，阴性预测值增加29%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
