Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer
Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer
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DOI：
10.1093/humrep/dez064
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发表时间：
2019-06-01
影响因子：
6.1
通讯作者：
Gardner, D. K.
中科院分区：
文献类型：
--
作者：
Tran, D.;Cooke, S.;Gardner, D. K.
研究问题：深度学习模型能否从延时视频中预测胎儿心脏（FH）怀孕的概率？总结回答：我们创建了一个名为IVY的深度学习模型，这是一个客观的全自动系统，可以直接从原始的延时视频中预测FH妊娠的概率，而不需要任何手动的形态动力学注释或囊胚形态评估。已知：延时成像在有效的胚胎选择中的贡献是有希望的。用于分析延时成像的现有算法是基于形态学和形态动力学参数的，其需要主观的人类注释，因此具有内在的读者间和读者内可变性。深度学习为胚胎选择的自动化和标准化提供了希望。研究设计、规模、持续时间：回顾性分析2014年1月至2018年12月期间来自4个不同国家8个不同IVF诊所的10638个胚胎的延时视频和临床结局。参与者/材料、环境、方法：使用具有已知FH妊娠结果的延时视频训练深度学习模型，以执行预测给定延时视频序列的FH妊娠概率的二元分类任务。模型的预测能力是使用5倍分层交叉验证的受试者工作特征曲线的平均曲线下面积（AUC）来衡量的。主要结果和机会的作用：深度学习模型能够从延时视频中预测FH妊娠，在5倍分层交叉验证中AUC为0. 93 [95% CI 0. 92 - 0. 94]。在8个实验室中进行的保留验证测试表明，AUC具有可重复性，在不同培养和实验室流程的不同实验室中，AUC范围为0.95至0.90。局限性，避免的原因：本研究是一项回顾性分析，表明深度学习模型对胚胎植入的可能性具有高度的可预测性。这些发现的临床影响仍不确定。需要进一步的研究，包括前瞻性随机对照试验，以评估这种深度学习模型的临床意义。收集用于训练和验证的延时视频是第5天的胚胎;因此，需要对第3天移植的模型进行额外的调整。研究结果的更广泛意义：深度学习模型获得的胚胎植入的高预测值可能会提高以前用于胚胎选择的延时成像方法的有效性。这可以改善单个胚胎移植的最有活力的胚胎的优先级。深度学习模型也可能被证明在为冷冻保存胚胎的后续转移提供最佳顺序方面是有用的。
STUDY QUESTION: Can a deep learning model predict the probability of pregnancy with fetal heart (FH) from time-lapse videos?SUMMARY ANSWER: We created a deep learning model named IVY, which was an objective and fully automated system that predicts the probability of FH pregnancy directly from raw time-lapse videos without the need for any manual morphokinetic annotation or blastocyst morphology assessment.WHAT IS KNOWN ALREADY: The contribution of time-lapse imaging in effective embryo selection is promising. Existing algorithms for the analysis of time-lapse imaging are based on morphology and morphokinetic parameters that require subjective human annotation and thus have intrinsic inter-reader and intra-reader variability. Deep learning offers promise for the automation and standardization of embryo selection.STUDY DESIGN, SIZE, DURATION: A retrospective analysis of time-lapse videos and clinical outcomes of 10 638 embryos from eight different IVF clinics, across four different countries, between January 2014 and December 2018.PARTICIPANTS/MATERIALS, SETTING, METHODS: The deep learning model was trained using time-lapse videos with known FH pregnancy outcome to perform a binary classification task of predicting the probability of pregnancy with FH given time-lapse video sequence. The predictive power of the model was measured using the average area under the curve (AUC) of the receiver operating characteristic curve over 5-fold stratified cross-validation.MAIN RESULTS AND THE ROLE OF CHANCE: The deep learning model was able to predict FH pregnancy from time-lapse videos with an AUC of 0.93 [95% CI 0.92-0.94] in 5-fold stratified cross-validation. A hold-out validation test across eight laboratories showed that the AUC was reproducible, ranging from 0.95 to 0.90 across different laboratories with different culture and laboratory processes.LIMITATIONS, REASONS FOR CAUTION: This study is a retrospective analysis demonstrating that the deep learning model has a high level of predictability of the likelihood that an embryo will implant. The clinical impacts of these findings are still uncertain. Further studies, including prospective randomized controlled trials, are required to evaluate the clinical significance of this deep learning model. The time-lapse videos collected for training and validation are Day 5 embryos; hence, additional adjustment would need to be made for the model to be used in the context of Day 3 transfer.WIDER IMPLICATIONS OF THE FINDINGS: The high predictive value for embryo implantation obtained by the deep learning model may improve the effectiveness of previous approaches used for time-lapse imaging in embryo selection. This may improve the prioritization of the most viable embryo for a single embryo transfer. The deep learning model may also prove to be useful in providing the optimal order for subsequent transfers of cryopreserved embryos.