Shen Lei, Zhang Yanran, Chen Wenfeng, Yin Xinghui
College of Computer and Information, Hohai University, Nanjing, China.
Nanjing Marine Radar Institute, Nanjing, China.
Front Physiol. 2022 Jun 30;13:885661. doi: 10.3389/fphys.2022.885661. eCollection 2022.
Recurrent implantation failure (RIF) refers to that infertile patients have undergone multiple fertilization (IVF) or intracytoplasmic sperm injection (ICSI) cycles and transferred multiple embryos without embryo implantation or clinical pregnancy. Due to the lack of clear evidence-based medical guidelines for the number of embryos to be transferred in RIF patients, how to obtain the highest single cycle pregnancy success rate with as few embryos transferred as possible while avoiding multiple pregnancy as much as possible, that is, how to balance the pregnancy success rate and multiple pregnancy rate, is a great challenge for clinicians and RIF patients. We urgently need an effective and reliable assisted decision-making method to help clinicians find this balance, and an artificial intelligence (AI) system will provide an efficient solution. In this research, we filtered out the RIF data set ( = 45,921) from the Human Fertilisation and Embryology Authority (HFEA) database from 2005 to 2016. The data set was divided into two groups according to the number of embryos transferred, Group A and B. Group A included 34,175 cycles with two embryos transferred, while Group B included 11,746 cycles with only one embryo transferred, each containing 44 features and a prediction label (pregnancy). Four machine learning algorithms (RF, GBDT, AdaBoost, and MLP) were used to train Group A and Group B data set respectively and 10-folder cross validation method was used to validate the models. The results revealed that the AdaBoost model of Group A obtained the best performance, while the GBDT model in Group B was proved to be the best model. Both models had been proved to provide accurate prediction of transfer outcome. Our research provided a new approach for targeted and personalized treatment of RIF patients to help them achieve efficient and reliable pregnancy. And an AI-assisted decision-making system will be designed to help clinicians and RIF patients develop personalized transfer strategies, which not only guarantees efficient and reliable pregnancy, but also avoids the risk of multiple pregnancy as much as possible.
反复种植失败（RIF）是指不孕患者经历了多次体外受精（IVF）或卵胞浆内单精子注射（ICSI）周期，并移植了多个胚胎，但仍未实现胚胎着床或临床妊娠。由于缺乏关于RIF患者移植胚胎数量的明确循证医学指南，如何在尽可能少移植胚胎的情况下获得最高的单周期妊娠成功率，同时尽可能避免多胎妊娠，即如何平衡妊娠成功率和多胎妊娠率，对临床医生和RIF患者来说是一个巨大的挑战。我们迫切需要一种有效且可靠的辅助决策方法来帮助临床医生找到这种平衡，而人工智能（AI）系统将提供一个高效的解决方案。在本研究中，我们从人类受精与胚胎学管理局（HFEA）2005年至2016年的数据库中筛选出RIF数据集（n = 45921）。该数据集根据移植胚胎数量分为两组，A组和B组。A组包括34175个移植两个胚胎的周期，而B组包括11746个仅移植一个胚胎的周期，每组包含44个特征和一个预测标签（妊娠）。分别使用四种机器学习算法（随机森林（RF）、梯度提升决策树（GBDT）、自适应增强（AdaBoost）和多层感知器（MLP））对A组和B组数据集进行训练，并采用十折交叉验证方法对模型进行验证。结果显示，A组的AdaBoost模型表现最佳，而B组的GBDT模型被证明是最佳模型。两个模型均被证明能准确预测移植结果。我们的研究为RIF患者的靶向和个性化治疗提供了一种新方法，以帮助他们实现高效且可靠的妊娠。并且将设计一个AI辅助决策系统，以帮助临床医生和RIF患者制定个性化的移植策略，这不仅能保证高效且可靠的妊娠，还能尽可能避免多胎妊娠的风险。
