Mirroshandel Seyed Abolghasem, Ghasemian Fatemeh, Monji-Azad Sara
Department of Computer Engineering, University of Guilan, Rasht, Iran.
Department of Biology, University of Guilan, Rasht, Iran.
Comput Methods Programs Biomed. 2016 Dec;137:215-229. doi: 10.1016/j.cmpb.2016.09.013. Epub 2016 Sep 26.
Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection.
The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process.
In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time.
A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment.
在卵胞浆内单精子注射（ICSI）过程中获取高质量精子是主要关注点之一。了解单个精子形态对受精、胚胎质量和妊娠概率的影响是男性因素不孕症最重要的课题之一。胚胎学家需要在ICSI周期中实时决定最佳的注射精子。我们的目标是在ICSI周期中为男性因素不孕症患者注射每个精子之前预测合子、胚胎的质量以及着床结果，旨在提供一个精子选择的决策支持系统。
收集了2012年至2014年在拉什特阿尔扎赫拉医院不孕治疗中心的219例男性因素不孕症患者的信息。所准备的数据集包括向相关卵母细胞注射的1544个精子的合子、胚胎质量以及着床结果。在我们的研究中，在第3天进行胚胎移植。每个精子由13个临床特征表示。数据预处理是所提出的数据挖掘算法的第一步。在应用了30多个分类器后，选择了9个成功的分类器，并使用精度、召回率、F1和AUC指标通过10折交叉验证技术进行评估。另一个重要实验是测量每个特征在预测过程中的作用。
在合子和胚胎质量预测中，IBK模型和随机委员会模型的F1分别为79.2%和83.8%。在着床结果预测中，KStar模型的F1达到95.9%，甚至优于人类专家的预测。所有这些预测都可以实时完成。
基于机器学习的决策支持系统将有助于ICSI周期的精子选择阶段，以提高ICSI治疗的成功率。
