BML Munjal University, Gurugram, 122413, India.
Sci Rep. 2020 Dec 1;10(1):20925. doi: 10.1038/s41598-020-76928-z.
In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to penetrate or survive in the cervical mucus and low sperm counts, resulting human infertility. Nevertheless, IVF does not guarantee success in the fertilization. Choosing IVF is burdensome for the reason of high cost and uncertainty in the result. As the complications and fertilization factors are numerous in the IVF process, it is a cumbersome task for fertility doctors to give an accurate prediction of a successful birth. Artificial Intelligence (AI) has been employed in this study for predicting the live-birth occurrence. This work mainly focuses on making predictions of live-birth occurrence when an embryo forms from a couple and not a donor. Here, we compare various AI algorithms, including both classical Machine Learning, deep learning architecture, and an ensemble of algorithms on the publicly available dataset provided by Human Fertilisation and Embryology Authority (HFEA). Insights on data and metrics such as confusion matrices, F1-score, precision, recall, receiver operating characteristic (ROC) curves are demonstrated in the subsequent sections. The training process has two settings Without feature selection and With feature selection to train classifier models. Machine Learning, Deep learning, ensemble models classification paradigms have been trained in both settings. The Random Forest model achieves the highest F1-score of 76.49% in without feature selection setting. For the same model, the precision, recall, and area under the ROC Curve (ROC AUC) scores are 77%, 76%, and 84.60%, respectively. The success of the pregnancy depends on both male and female traits and living conditions. This study predicts a successful pregnancy through the clinically relevant parameters in In-vitro fertilization. Thus artificial intelligence plays a promising role in decision making process to support the diagnosis, prognosis, treatment etc.
体外受精（IVF）是解决子宫内膜异位症、卵子质量差、父母遗传疾病、排卵问题、损害精子或卵子的抗体问题、精子无法穿透或在宫颈粘液中存活以及精子数量低等导致人类不孕的并发症的常用方法。然而，体外受精并不能保证受精成功。选择体外受精的原因是成本高，结果不确定。由于体外受精过程中的并发症和受精因素众多，生育医生要准确预测成功生育是一项繁琐的任务。本研究采用人工智能（AI）来预测活产的发生。这项工作主要侧重于预测一对夫妇而不是捐赠者的胚胎形成时活产的发生。在这里，我们比较了各种 AI 算法，包括经典机器学习、深度学习架构以及在人类受精和胚胎学管理局（HFEA）提供的公开数据集上的算法集合。后续部分展示了数据和指标的见解，例如混淆矩阵、F1 分数、精度、召回率、接收者操作特征（ROC）曲线。训练过程有两个设置：无特征选择和有特征选择，用于训练分类器模型。在这两个设置中，机器学习、深度学习、集成模型分类范例已被训练。随机森林模型在无特征选择设置中的 F1 分数最高，为 76.49%。对于相同的模型，精度、召回率和 ROC 曲线下的面积（ROC AUC）得分分别为 77%、76%和 84.60%。妊娠的成功取决于男性和女性的特征和生活条件。本研究通过体外受精中的临床相关参数预测成功妊娠。因此，人工智能在决策过程中发挥着有前途的作用，可支持诊断、预后、治疗等。
