Consistency and objectivity of automated embryo assessments using deep neural networks.
Consistency and objectivity of automated embryo assessments using deep neural networks.
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DOI：
10.1016/j.fertnstert.2019.12.004
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发表时间：
2020-04
影响因子：
6.7
通讯作者：
Shafiee H
中科院分区：
文献类型：
--
作者：
Bormann CL;Thirumalaraju P;Kanakasabapathy MK;Kandula H;Souter I;Dimitriadis I;Gupta R;Pooniwala R;Shafiee H
评价深层神经网络在胚胎评分和活检和冷冻保存处置决策中的一致性和客观性，并与训练有素的胚胎学家进行的分级进行比较。使用回顾性数据的前瞻性双盲研究。美国--大型学术生育中心。不适用因胚胎图像（748个记录在授精后70小时[hpi]）和742个记录在113 hpi）被用来评估胚胎学家和神经网络在胚胎分级。10个胚胎学家和神经网络的性能进行了评价，在处置决策使用56个胚胎。比较变异系数（%CV）和变异系数的测量值。胚胎学家在胚胎分级中表现出高度的变异性（%CV平均值：70 hpi为82.84%，113 hpi为44.98%）。当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%。神经网络在选择囊胚进行活检和冷冻保存方面优于胚胎学家，一致性为83.92%。Cronbach 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。我们的研究结果显示，在对胚胎进行评分时，胚胎学家间和胚胎学家内的差异性很高，这可能是由于传统形态学分级的主观性。这可能最终导致不太精确的处置决定和丢弃可行的胚胎。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，从而可能改善胚胎学实验室的结果。
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. Prospective double-blind study using retrospective data. U.S.-based large academic fertility center. Not applicable. 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. Coefficients of variation (%CV) and measures of consistencies were compared. 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. 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.
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影响因子：
15.2
作者：
Khosravi, Pegah;Kazemi, Ehsan;Hajirasouliha, Iman
通讯作者：
Hajirasouliha, Iman
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
6.1
作者：
Tran, D.;Cooke, S.;Gardner, D. K.
通讯作者：
Gardner, D. K.
影响因子：
--
作者：
Bland, JM;Altman, DG
通讯作者：
Altman, DG
影响因子：
6.1
作者：
Potluri, Vaishnavi;Kathiresan, Preethi Sangeetha;Shafiee, Hadi
通讯作者：
Shafiee, Hadi