PDF(Pubmed)
Abstract：
Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.
摘要：
男性不育是一个相关的公共卫生问题，但是到目前为止，还没有对不同的机器学习（ML）模型及其准确性进行系统的审查。本综述旨在全面研究ML算法在预测男性不育中的应用。因此报告了所使用的模型在预测男性不育症作为主要结局的准确性。将特别注意人工神经网络（ANN）的使用。在PubMed进行了全面的文献检索，Scopus,和科学直接在2023年7月15日至10月23日之间，根据系统审查和荟萃分析（PRISMA）指南的首选报告项目进行。我们使用推荐的工具对纳入的研究进行了质量评估，该工具建议用于所采用的研究设计类型。我们还对与纳入研究相关的偏倚风险（RoB）进行了筛查。因此，43相关出版物被纳入这篇综述，总共检测到40种不同的ML模型。这些研究包括报道了良好的质量，即使RoB并不总是适合所有类型的研究。纳入的研究报告，使用ML模型预测男性不育的平均准确率为88%。我们发现只有七项研究使用ANN模型预测男性不育，报告中值准确率为84%。
