Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep-Learning
Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep-Learning
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DOI：
10.1002/aisy.202000080
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发表时间：
2020-10-01
影响因子：
7.4
通讯作者：
Buxboim, Amnon
中科院分区：
文献类型：
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作者：
Kan-Tor, Yoav;Zabari, Nir;Buxboim, Amnon
在体外受精（IVF）治疗中，需要早期识别具有高植入潜力的胚胎，以缩短怀孕时间，同时避免多胎妊娠对新生儿和母亲造成的临床并发症。目前的分类工具是基于形态和形态动力学参数，手动注释使用延时视频文件。然而，手动注释引入了观察者间和观察者内的变异性，并提供了一个离散的表示植入前发育，而忽略了与胚胎质量相关的动态特征。通过直接在>6200个囊胚标记和>5500个囊胚标记的胚胎的原始视频文件上训练深度神经网络，开发了一个完全自动化和标准化的分类器。胚胎植入的预测比目前最先进的形态学分类器更准确。胚胎分类随着视频长度的增加而改善，其中最具预测性的图像仅显示与形态特征的部分关联。因此，深度学习代替人类评估胚胎发育能力有助于实施单胚胎移植方法。
In in vitro fertilization (IVF) treatments, early identification of embryos with high implantation potential is required for shortening time to pregnancy while avoiding clinical complications to the newborn and the mother caused by multiple pregnancies. Current classification tools are based on morphological and morphokinetic parameters that are manually annotated using time-lapse video files. However, manual annotation introduces interobserver and intraobserver variability and provides a discrete representation of preimplantation development while ignoring dynamic features that are associated with embryo quality. A fully automated and standardized classifiers are developed by training deep neural networks directly on the raw video files of >6200 blastulation-labeled and >5500 implantation-labeled embryos. Prediction of embryo implantation is more accurate than the current state-of-the-art morphokientic classifier. Embryo classification improves with video length where the most predictive images show only partial association with morphological features. Deep learning substitute to human evaluation of embryo developmental competence thus contributes to implementing single embryo transfer methodology.