An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach.

IF 2.1 4区 医学 Q3 ANDROLOGY
Sanaa Badr, Meryem Tahri, Mohamed Maanan, Jan Kašpar, Noura Yousfi
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Abstract

Infertility has emerged as a significant public health concern, with assisted reproductive technology (ART) is a last-resort treatment option. However, ART's efficacy is limited by significant financial cost and physical discomfort. The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews. Binary classification models were developed to classify cases into two groups: those transferring two or fewer embryos and those transferring three or four. Four popular ML algorithms were used, including random forest (RF), logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN), considering seven criteria: the woman's age, sperm origin, the developmental qualities of four potential embryos, infertility duration, assessment of the woman, morphological qualities of the four best embryos on the day of transfer, and number of oocytes extracted. The stratified 3-fold cross-validation results show that the SVM model obtained the highest average accuracy (95.83%) and demonstrated the best overall performance, closely followed by the ANN and LR models with an average accuracy equal to 91.67%. The RF model achieved a slightly lower average accuracy (88.89%), which demonstrated the lowest variability. Testing on a new dataset revealed all models performed well, with ANN and SVM models classified all test set instances correctly, while the RF and LR models achieved 91.68% accuracy. These results highlight the superior generalization and effectiveness of the ANN and SVM models in guiding ART decisions.

生殖技术中胚胎移植的智能决策系统:基于机器学习的方法。
不孕不育已成为一个重大的公共卫生问题,辅助生殖技术(ART)是最后的治疗选择。然而,ART的疗效受到巨大的财务成本和身体不适的限制。本研究的目的是建立机器学习(ML)决策支持模型,使用通过文献综述确定的不育夫妇的数据来预测移植胚胎数量的最佳范围。建立了二元分类模型,将病例分为两组:移植两个或更少胚胎的病例和移植三个或四个胚胎的病例。采用随机森林(RF)、逻辑回归(LR)、支持向量机(SVM)和人工神经网络(ANN)等4种流行的ML算法,考虑7个标准:女性年龄、精子来源、4个潜在胚胎的发育质量、不孕持续时间、女性评估、移植当天4个最佳胚胎的形态质量和提取的卵母细胞数量。分层3重交叉验证结果表明,SVM模型平均准确率最高(95.83%),综合性能最佳,ANN和LR模型紧随其后,平均准确率为91.67%。RF模型的平均准确率略低(88.89%),这表明变异性最低。在新数据集上的测试表明,所有模型都表现良好,ANN和SVM模型对所有测试集实例的分类正确,而RF和LR模型的准确率达到91.68%。这些结果突出了ANN和SVM模型在指导ART决策方面的卓越泛化和有效性。
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来源期刊
CiteScore
4.30
自引率
4.20%
发文量
27
审稿时长
>12 weeks
期刊介绍: Systems Biology in Reproductive Medicine, SBiRM, publishes Research Articles, Communications, Applications Notes that include protocols a Clinical Corner that includes case reports, Review Articles and Hypotheses and Letters to the Editor on human and animal reproduction. The journal will highlight the use of systems approaches including genomic, cellular, proteomic, metabolomic, bioinformatic, molecular, and biochemical, to address fundamental questions in reproductive biology, reproductive medicine, and translational research. The journal publishes research involving human and animal gametes, stem cells, developmental biology and toxicology, and clinical care in reproductive medicine. Specific areas of interest to the journal include: male factor infertility and germ cell biology, reproductive technologies (gamete micro-manipulation and cryopreservation, in vitro fertilization/embryo transfer (IVF/ET) and contraception. Research that is directed towards developing new or enhanced technologies for clinical medicine or scientific research in reproduction is of significant interest to the journal.
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