Abstract

Culturing of human embryos in optimal conditions is crucial for a successful in vitro fertilisation (IVF) programme. In addition, the capacity to assess and rank embryos correctly for quality will allow for transfer of the potentially ‘best’ embryo first, thereby shortening the time to pregnancy, although not improving cumulative pregnancy and live birth rates. It will also encourage and facilitate the implementation of single embryo transfers, thereby increasing safety for mother and offspring. Time-lapse technology introduces the concept of stable culture conditions, in connection with the possibility of continuous viewing and documenting of the embryo throughout development. However, so far, even when embryo quality scoring is based on large datasets, or when using the time-lapse technology, the morphokinetic scores are still mainly based on subjective and intermittent annotations of morphology and timings. Also, the construction of powerful algorithms for widespread use is hampered by large variations in culture conditions between individual IVF laboratories. New methodology, involving machine learning, where every image from the time-lapse documentation is analysed by a computer programme, looking for patterns that link to outcome, may in the future provide a more accurate and non-biased embryo selection.

摘要

在最佳条件下培养人类胚胎对于成功的体外培养至关重要受精（IVF）计划。此外，正确评估和鉴定胚胎质量的能力将允许首先转移潜在的“最佳”胚胎，从而缩短怀孕时间，尽管不会提高累计怀孕率和活产率。它还将鼓励并促进单胚胎移植的实施，从而提高母亲和后代的安全性。延时技术引入了稳定培养条件的概念，以及在整个发育过程中连续观察和记录胚胎的可能性。但是，到目前为止，即使当胚胎质量评分基于大型数据集或使用延时技术时，形态动力学评分仍主要基于形态学和时机的主观和间歇性注释。也，各个IVF实验室之间培养条件的巨大差异阻碍了广泛使用的强大算法的构建。涉及机器学习的新方法，其中通过计算机程序分析延时文档中的每个图像，寻找与结果相关的模式，将来可能会提供更准确且无偏见的胚胎选择。