Medical AI, Harrison AI, Barangaroo, NSW, Australia.
Embryology, IVF Australia, Greenwich, NSW, Australia.
Hum Reprod. 2019 Jun 4;34(6):1011-1018. doi: 10.1093/humrep/dez064.
Can a deep learning model predict the probability of pregnancy with fetal heart (FH) from time-lapse videos?
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.
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.
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.
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.
STUDY FUNDING/COMPETING INTEREST(S): D.T. is the co-owner of Harrison AI that has patented this methodology in association with Virtus Health. P.I. is a shareholder in Virtus Health. S.C., P.I. and D.G. are all either employees or contracted with Virtus Health. D.G. has received grant support from Vitrolife, the manufacturer of the Embryoscope time-lapse imaging used in this study. The equipment and time for this study have been jointly provided by Harrison AI and Virtus Health.
深度学习模型能否通过延时视频预测具有胎心（FH）的妊娠概率？
我们创建了一个名为 IVY 的深度学习模型，它是一个客观且完全自动化的系统，可直接从原始延时视频中预测 FH 妊娠的概率，而无需任何手动形态动力学注释或囊胚形态评估。
延时成像在有效胚胎选择中的作用具有很大的潜力。现有的延时成像分析算法基于形态和形态动力学参数，这些参数需要主观的人工注释，因此具有内在的读者间和读者内变异性。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、规模和持续时间：这是一项回顾性分析，对 2014 年 1 月至 2018 年 12 月期间来自四个不同国家的八家不同试管婴儿诊所的 10638 个胚胎的延时视频和临床结果进行了分析。
参与者/材料、设置和方法：该深度学习模型使用具有已知 FH 妊娠结局的延时视频进行训练，以执行给定延时视频序列预测具有 FH 妊娠概率的二元分类任务。使用接收器工作特征曲线的平均曲线下面积（AUC）在 5 折分层交叉验证中衡量模型的预测能力。
该深度学习模型能够通过延时视频预测 FH 妊娠，在 5 折分层交叉验证中 AUC 为 0.93[95%CI 0.92-0.94]。在跨越八个实验室的留一验证测试中，AUC 具有可重复性，在不同的实验室中，AUC 在 0.95 到 0.90 之间变化，这些实验室具有不同的培养和实验室过程。
局限性、谨慎的原因：本研究是一项回顾性分析，表明深度学习模型对胚胎植入可能性具有很高的预测水平。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。用于训练和验证的延时视频是第 5 天的胚胎；因此，需要进行额外的调整，以使模型能够用于第 3 天的转移。
深度学习模型获得的胚胎着床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。这可能会提高对最具活力的胚胎进行单胚胎移植的优先级。深度学习模型也可能被证明在提供后续冷冻胚胎转移的最佳顺序方面有用。
研究资金/竞争利益：D.T.是 Harrison AI 的共同所有者，该公司已将该方法与 Virtus Health 联合专利。P.I.是 Virtus Health 的股东。S.C.、P.I.和 D.G.均为 Virtus Health 的员工或签约员工。D.G.已从该研究中使用的 Embryoscope 延时成像的制造商 Vitrolife 获得资助。这项研究的设备和时间是由 Harrison AI 和 Virtus Health 共同提供的。
