Department of Computer Technology (DTIC), University of Alicante, Carretera San Vicente s/n, Alicante 03690, Spain.
Department of Computer Technology (DTIC), University of Alicante, Carretera San Vicente s/n, Alicante 03690, Spain.
Comput Methods Programs Biomed. 2022 Jun;219:106740. doi: 10.1016/j.cmpb.2022.106740. Epub 2022 Mar 10.
Mode of delivery is one of the issues that most concerns obstetricians. The caesarean section rate has increased progressively in recent years, exceeding the limit recommended by health institutions. Obstetricians generally lack the necessary technology to help them decide whether a caesarean delivery is appropriate based on antepartum and intrapartum conditions.
In this study, we have tested the suitability of using three popular artificial intelligence algorithms, Support Vector Machines, Multilayer Perceptron and, Random Forest, to develop a clinical decision support system for the prediction of the mode of delivery according to three categories: caesarean section, euthocic vaginal delivery and, instrumental vaginal delivery. For this purpose, we used a comprehensive clinical database consisting of 25,038 records with 48 attributes of women who attended to give birth at the Service of Obstetrics and Gynaecology of the University Clinical Hospital "Virgen de la Arrixaca" in the Murcia Region (Spain) from January of 2016 to January 2019. Women involved were patients with singleton pregnancies who attended to the emergency room on active labour or undergoing a planned induction of labour for medical reasons.
The three implemented algorithms showed a similar performance, all of them reaching an accuracy equal to or above 90% in the classification between caesarean and vaginal deliveries and somewhat lower, around 87% between instrumental and euthocic.
The results validate the use of these algorithms to build a clinical decision system to help gynaecologists to predict the mode of delivery.
分娩方式是妇产科医生最关心的问题之一。近年来，剖宫产率逐渐上升，超过了医疗机构建议的限度。妇产科医生通常缺乏必要的技术，无法根据产前和产时的情况来判断是否需要进行剖宫产。
在这项研究中，我们测试了三种流行的人工智能算法（支持向量机、多层感知机和随机森林）在开发临床决策支持系统以预测分娩方式方面的适用性，分为剖宫产、顺产和器械助产三种方式。为此，我们使用了一个综合的临床数据库，该数据库包含了 25038 名在 2016 年 1 月至 2019 年 1 月期间在西班牙穆尔西亚地区大学临床医院“Virgen de la Arrixaca”妇产科就诊的单胎妊娠妇女的 48 个特征的记录。纳入的妇女是因急诊或因医疗原因计划引产而到急诊室就诊的单胎妊娠妇女。
三种实现的算法表现相似，在剖宫产和阴道分娩之间的分类中，它们的准确率都达到或超过 90%，而在器械助产和顺产之间的准确率则略低，约为 87%。
研究结果验证了使用这些算法来构建临床决策系统以帮助妇科医生预测分娩方式的可行性。
