Performance of a deep learning based neural network in the selection of human blastocysts for implantation.
Performance of a deep learning based neural network in the selection of human blastocysts for implantation.
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DOI：
10.7554/elife.55301
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发表时间：
2020-09-15
期刊：
影响因子：
7.7
通讯作者：
Shafiee H
中科院分区：
文献类型：
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作者：
Bormann CL;Kanakasabapathy MK;Thirumalaraju P;Gupta R;Pooniwala R;Kandula H;Hariton E;Souter I;Dimitriadis I;Ramirez LB;Curchoe CL;Swain J;Boehnlein LM;Shafiee H
目前正在评估体外受精中的深度学习在辅助工具开发中的应用，以确定转移顺序和植入潜力，这些辅助工具使用通过昂贵的成像硬件收集的延时数据。然而，可以处理静态图像的辅助工具和算法可以帮助改善获得护理的机会，因为它们可以与几乎所有生育中心都可以使用的传统显微镜获得的图像一起使用。在这里，我们评估了深度卷积神经网络（CNN）在97个临床患者队列（742个胚胎）中胚胎选择中的使用，该网络使用人工授精后113小时收集的胚胎的单时间点图像进行训练，并观察到选择最高质量胚胎的准确率为90%。此外，训练有素的CNN使用一组97个能够着床的整倍体胚胎直接评估胚胎的着床潜力，优于来自五个不同生育中心的15名训练有素的胚胎学家（75.26%对67.35%,p<0.0001）。大约七分之一的夫妇怀孕有困难，这意味着对体外受精（IVF）等解决方案的需求很大。这个过程包括在实验室中使胚胎受精和发育，然后选择一些植入病人的子宫。然而，试管婴儿的成功率只有30%，而且价格昂贵，对患者来说可能会造成精神和身体上的负担。因此，选择合适的胚胎植入是极其重要的，因为这增加了成功的机会，最大限度地减少了并发症，并确保了婴儿的健康。目前可用于做出这一决定的工具是有限的、高度主观的、耗时的，而且往往非常昂贵。因此，胚胎学家在选择植入哪个胚胎时往往依赖于他们的经验和观察技能，这可能会导致很多变化。因此，基于人工智能（AI）的自动化系统可以通过帮助胚胎学家做出这一决定并确保更一致的结果来提高试管婴儿成功率。人工智能系统可以了解胚胎随着时间的推移是如何发育的，然后利用这些信息从一张图像中选择最好的胚胎进行植入。这将为目前只能在最昂贵的试管婴儿诊所使用的分析工具提供一种更便宜的选择。现在，Bormann, Kanakasabapathy， Thirumalaraj等人基于数千张胚胎图像开发了一种用于体外受精的人工智能系统。利用个人图像，该系统选择的胚胎质量与人类专家选择的胚胎质量相当。它还显示出更强的识别胚胎的能力，这些胚胎将导致成功的植入。事实上，在从一组几乎没有明显差异的高质量胚胎中检测出最有可能植入的胚胎方面，该软件的表现超过了来自美国五个不同中心的15名胚胎学家。人工智能在支持专家临床决策方面有许多潜在的应用。像这样的系统可以提高成功率，减少错误，并带来更快、更便宜和更容易获得的结果。除了直接的体外受精应用，该系统还可以用于研究和工业，以帮助了解胚胎质量的差异。
Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hr post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available. Furthermore, a CNN trained to assess an embryo’s implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p<0.0001) from five different fertility centers. Around one in seven couples have trouble conceiving, which means there is a high demand for solutions such as in vitro fertilization, also known as IVF. This process involves fertilizing and developing embryos in the laboratory and then selecting a few to implant into the womb of the patient. IVF, however, only has a 30% success rate, is expensive and can be both mentally and physically taxing for patients. Selecting the right embryos to implant is therefore extremely important, as this increases the chance of success, minimizes complications and ensures the baby will be healthy. Currently the tools available for making this decision are limited, highly subjective, time-consuming, and often extremely expensive. As a result, embryologists often rely on their experience and observational skills when choosing which embryos to implant, which can lead to a lot of variability. An automated system based on artificial intelligence (AI) could therefore improve IVF success rates by assisting embryologists with this decision and ensuring more consistent results. The AI system could learn how embryos develop over time and then uses this information to select the best embryos to implant from just a single image. This would offer a cheaper alternative to current analysis tools that are only available at the most expensive IVF clinics. Now, Bormann, Kanakasabapathy, Thirumalaraj et al. have developed an AI system for IVF based on thousands of images of embryos. Using individual images, the system selected embryos of a comparable quality to those selected by a human specialist. It also showed a greater ability to identify embryos that will lead to successful implantation. Indeed, the software outperformed 15 embryologists from five different centers across the United States in detecting which embryos were most likely to implant out of a group of high-quality embryos with few visible differences. Artificial intelligence has many potential applications to support expert clinical decision-making. Systems like these could improve success, reduce errors and lead to faster, cheaper and more accessible results. Beyond immediate IVF applications, this system could also be used in research and industry to help understand differences in embryo quality.