Raef Behnaz, Ferdousi Reza
Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Acta Inform Med. 2019 Sep;27(3):205-211. doi: 10.5455/aim.2019.27.205-211.
Assisted reproductive technologies (ART) are recent improvements in infertility treatment. However, there is no significant increase in pregnancy rates with the aid of ART. Costly and complex process of ART's makes them as challenging issues. Computational prediction models could predict treatment outcome, before the start of an ART cycle.
This review provides an overview on machine learning-based prediction models in ART.
This article was executed based on a literature review through scientific databases search such as PubMed, Scopus, Web of Science and Google Scholar.
We identified 20 papers reporting on machine learning-based prediction models in IVF or ICSI settings. All of the models were validated only by internal validation. Therefore, external validation of the models and the impact analysis of them were the missing parts of the all studies.
Machine learning-based prediction models provide a clinical decision support tool for both clinicians and patients and lead to improvement in ART success rates.
辅助生殖技术（ART）是不孕症治疗领域近期的进展。然而，借助ART并没有使妊娠率显著提高。ART成本高昂且过程复杂，这使其成为具有挑战性的问题。计算预测模型可以在ART周期开始前预测治疗结果。
本综述概述了ART中基于机器学习的预测模型。
本文通过在PubMed、Scopus、Web of Science和谷歌学术等科学数据库中进行文献检索来完成。
我们确定了20篇报告体外受精（IVF）或卵胞浆内单精子注射（ICSI）环境下基于机器学习预测模型的论文。所有模型都仅通过内部验证进行了验证。因此，模型的外部验证及其影响分析是所有研究中缺失的部分。
基于机器学习的预测模型为临床医生和患者提供了临床决策支持工具，并有助于提高ART成功率。
