Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
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DOI：
10.1038/s41746-019-0096-y
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发表时间：
2019-04-04
影响因子：
15.2
通讯作者：
Hajirasouliha, Iman
中科院分区：
文献类型：
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作者：
Khosravi, Pegah;Kazemi, Ehsan;Hajirasouliha, Iman
在体外受精(IVF)后，视觉形态评价是评价胚胎质量和选择移植胚泡的常规方法。然而，评估在胚胎学家之间产生了不同的结果，因此，试管受精的成功率仍然很低。为了克服胚胎质量的不确定性，通常会植入多个胚胎，导致意外的多胎妊娠和并发症。与其他成像领域不同，人类胚胎学和试管受精尚未利用人工智能(AI)进行无偏见、自动化的胚胎评估。我们推测，对数千个胚胎进行训练的人工智能方法可以在没有人类干预的情况下可靠地预测胚胎质量。我们实施了一种基于深度神经网络(DNN)的人工智能方法，使用来自美国一家大容量生育中心的大量人类胚胎延时图像(约50,000张)来选择最高质量的胚胎。我们开发了一个基于Google的初始模型的框架(Stork)。斯托克预测囊胚质量的AUC为0.98，并能很好地概括美国以外其他物种的图像，表现优于个别胚胎学家。使用2182个胚胎的临床数据，我们创建了一个决策树，以整合胚胎质量和患者年龄，以确定与怀孕可能性相关的情景。我们的分析表明，基于单个胚胎的受孕几率从13.8%(年龄=41岁，质量不佳)到66.3%(年龄)不等
Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google's Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinks outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age >= 41 and poor-quality) to 66.3% (age