基于深度学习的卵裂期单时间点图像预测胚胎发育结局
Deep learning mediated single time-point image-based prediction of embryo developmental outcome at the cleavage stage
摘要：
在常规的临床体外受精实践中，胚胎在发育的卵裂或胚泡阶段被移植。特别是，卵裂期移植对于预后相对较差的患者和资源有限的生育中心是有益的，在这些中心，体外胚胎发育失败的可能性更高。然而，在卵裂阶段胚胎选择的主要限制之一是可用于预测发育结果的手动可辨别特征的数量非常少。虽然，延时成像系统已经被提出作为可能的解决方案，但是它们是成本高昂的，并且需要庞大且昂贵的硬件，并且是劳动密集型的。卷积神经网络（CNN）的进步已经被用于提供跨许多医疗和非医疗对象类别的准确分类。在这里，我们报告了一个自动化系统，用于分类和选择人类胚胎在卵裂阶段使用一个训练有素的CNN结合遗传算法。该系统选择了受精后70小时（hpi）的卵裂期胚胎，最终在70 hpi时发育成优质囊胚，准确率为64%，超过了胚胎学家识别具有最高发育潜力的胚胎的能力。这些系统可以对IVF程序产生重大影响，使胚胎学家能够在资源贫乏和资源丰富的环境中进行准确和一致的胚胎评估。
英文摘要：
In conventional clinical in-vitro fertilization practices embryos are transferred either at the cleavage or blastocyst stages of development. Cleavage stage transfers, particularly, are beneficial for patients with relatively poor prognosis and at fertility centers in resource-limited settings where there is a higher chance of developmental failure in embryos in-vitro. However, one of the major limitations of embryo selections at the cleavage stage is the availability of very low number of manually discernable features to predict developmental outcomes. Although, time-lapse imaging systems have been proposed as possible solutions, they are cost-prohibitive and require bulky and expensive hardware, and labor-intensive. Advances in convolutional neural networks (CNNs) have been utilized to provide accurate classifications across many medical and non-medical object categories. Here, we report an automated system for classification and selection of human embryos at the cleavage stage using a trained CNN combined with a genetic algorithm. The system selected the cleavage stage embryo at 70 hours post insemination (hpi) that ultimately developed into top-quality blastocyst at 70 hpi with 64% accuracy, outperforming the abilities of embryologists in identifying embryos with the highest developmental potential. Such systems can have a significant impact on IVF procedures by empowering embryologists for accurate and consistent embryo assessment in both resource-poor and resource-rich settings.
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。
