OBJECTIVE To evaluate the consistency and objectivity of deep neural networks in embryo scoring and making disposition decisions for biopsy and cryopreservation in comparison to grading by highly trained embryologists. DESIGN Prospective double-blind study using retrospective data. SETTING U.S.-based large academic fertility center. PATIENTS Not applicable. INTERVENTION(S) Embryo images (748 recorded at 70 hours postinsemination [hpi]) and 742 at 113 hpi) were used to evaluate embryologists and neural networks in embryo grading. The performance of 10 embryologists and a neural network were also evaluated in disposition decision making using 56 embryos. MAIN OUTCOME MEASURES Coefficients of variation (%CV) and measures of consistencies were compared. RESULTS Embryologists exhibited a high degree of variability (%CV averages: 82.84% for 70 hpi and 44.98% for 113 hpi) in grading embryo. When selecting blastocysts for biopsy or cryopreservation, embryologists had an average consistency of 52.14% and 57.68%, respectively. The neural network outperformed the embryologists in selecting blastocysts for biopsy and cryopreservation with a consistency of 83.92%. Cronbach's α analysis revealed an α coefficient of 0.60 for the embryologists and 1.00 for the network. CONCLUSIONS The results of our study show a high degree of interembryologist and intraembryologist variability in scoring embryos, likely due to the subjective nature of traditional morphology grading. This may ultimately lead to less precise disposition decisions and discarding of viable embryos. The application of a deep neural network, as shown in our study, can introduce improved reliability and high consistency during the process of embryo selection and disposition, potentially improving outcomes in an embryology laboratory.

中文翻译：

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使用深度神经网络进行自动化胚胎评估的一致性和客观性

目的 与训练有素的胚胎学家进行分级相比，评估深度神经网络在胚胎评分以及活检和冷冻保存处置决策中的一致性和客观性。使用回顾性数据设计前瞻性双盲研究。设置位于美国的大型学术生育中心。患者 不适用。干预 胚胎图像（在授精后 70 小时 [hpi] 记录的 748 张）和 113 hpi 记录的 742 张）用于评估胚胎学家和神经网络在胚胎分级中的情况。还对 10 名胚胎学家和神经网络在使用 56 个胚胎的处置决策中的表现进行了评估。主要结果指标 比较了变异系数 (%CV) 和一致性指标。结果 胚胎学家在胚胎分级方面表现出高度的变异性（％CV 平均值：70 hpi 为 82.84%，113 hpi 为 44.98%）。在选择囊胚进行活检或冷冻保存时，胚胎学家的平均一致性分别为52.14%和57.68%。该神经网络在选择用于活检和冷冻保存的囊胚方面优于胚胎学家，一致性为 83.92%。Cronbach 的 α 分析显示胚胎学家的 α 系数为 0.60，网络的 α 系数为 1.00。结论 我们的研究结果显示，胚胎学家和胚胎内学家在对胚胎进行评分时存在高度的变异性，这可能是由于传统形态分级的主观性所致。这最终可能导致处置决策不太精确并丢弃可存活的胚胎。正如我们的研究所示，深度神经网络的应用可以在胚胎选择和处置过程中提高可靠性和高度一致性，从而有可能改善胚胎学实验室的结果。