Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning approach, we have shown that network models pre-trained with high quality embryo image data can be re-trained using data recorded on such low-cost, portable optical systems for embryo assessment and classification when relatively low-resolution image data are used. Using two test sets of 272 and 319 embryo images recorded on the reported stand-alone and smartphone optical systems, we were able to classify embryos based on their cell morphology with >90% accuracy.

中文翻译：

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开发和评估廉价的基于深度学习的胚胎学自动化成像系统。

胚胎评估和选择是体外受精(IVF) 过程中的关键步骤。当前的胚胎评估方法，例如由胚胎学家或半自动延时成像系统进行的手动显微镜分析，非常主观、耗时或昂贵。用于胚胎图像数据采集和分析的经济高效且易于使用的硬件和软件的可用性可以显着帮助胚胎学家在资源有限和资源丰富的环境中做出更有效的临床决策。在这里，我们报告了两种廉价（<100 美元和<5 美元）和自动化成像平台的开发，这些平台利用人工智能 (AI) 的进步来快速、可靠和准确地评估胚胎形态质量。使用分层学习方法，我们已经证明，使用高质量胚胎图像数据预训练的网络模型可以使用这种低成本便携式光学系统上记录的数据进行重新训练，以便在图像数据分辨率相对较低时进行胚胎评估和分类被使用。使用在报告的独立和智能手机光学系统上记录的两个测试集（包含 272 个和 319 个胚胎图像），我们能够根据胚胎的细胞形态对胚胎进行分类，准确率 >90%。