The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190416, Israel.
The Center for Interdisciplinary Data Science Research, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, Israel.
J Assist Reprod Genet. 2023 Feb;40(2):309-322. doi: 10.1007/s10815-022-02619-5. Epub 2022 Oct 4.
First trimester miscarriage is a major concern in IVF-ET treatments, accounting for one out of nine clinical pregnancies and for up to one out of three recognized pregnancies. To develop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development.
Retrospective study of a 4-year multi-center cohort of 391 women undergoing intra-cytoplasmatic sperm injection (ICSI) and fresh single or double embryo transfers. The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live-birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester. Data were recorded and obtained in hospital setting and research was performed in university setting.
A minimal subset of six non-redundant morphodynamic features were screened that maintained high prediction capacity. Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome as evaluated using the SHapley Additive exPlanations (SHAP) methodology. Using this feature subset, XGBoost and random forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Miscarriage was predicted with AUC 0.68 to 0.69.
We report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential is expected to improve live-birth rates and shorten time-to-pregnancy.
早期流产是体外受精-胚胎移植（IVF-ET）治疗中的一个主要关注点，占临床妊娠的九分之一，占已确认妊娠的三分之一。本研究旨在开发一种基于胚胎植入前发育的延时成像的机器学习分类器，用于预测卵裂期胚胎发生早期流产的风险。
回顾性分析了 4 年来 391 名接受胞浆内单精子注射（ICSI）和新鲜单胚胎或双胚胎移植的妇女。该研究纳入了基于孕囊可视化具有临床种植阳性指征的胚胎，包括早期流产或活产结局。流产的确定基于孕早期未见胎心搏动。数据在医院环境中记录和获取，研究在大学环境中进行。
筛选出了 6 个非冗余的形态动力学特征，这些特征具有较高的预测能力。在利用 SHapley Additive exPlanations（SHAP）方法评估时，能够反映小原核和原核动态中核仁前体分布的特征，对流产结局具有高度预测性。使用这个特征子集，XGBoost 和随机森林模型在 100 倍的蒙特卡罗交叉验证方案下进行了训练。流产的预测 AUC 为 0.68 至 0.69。
我们报告了一种用于识别高流产风险胚胎的决策支持工具的开发。根据预测的流产风险和预测的着床潜力，优先选择胚胎进行移植，有望提高活产率并缩短妊娠时间。
