Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is that ultrasound estimation of fetal weight is subject to population’s difference, strict operating requirements for sonographers, and poor access to ultrasound in low-resource areas. Inaccurate estimations may lead to negative perinatal outcomes. This study aims to predict fetal weight at varying gestational age in the absence of ultrasound examination within a certain accuracy. We consider that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring. We present a robust methodology using a data set comprising 4212 intrapartum recordings. The cubic spline function is used to fit the curves of several key characteristics that are extracted from ultrasound reports. A number of simple and powerful machine learning algorithms are trained, and their performance is evaluated with real test data. We also propose a novel evaluation performance index called the intersection-over-union (loU) for our study. The results are encouraging using an ensemble model consisting of Random Forest, XGBoost, and LightGBM algorithms. The experimental results show the loU between predicted range of fetal weight at any gestational age that is given by the ensemble model and ultrasound respectively. The machine learning based approach applied in our study is able to predict, with a high accuracy, fetal weight at varying gestational age in the absence of ultrasound examination.

产科超声检查的生理参数已主要用于估计怀孕期间的胎儿体重和分娩前的婴儿体重，以监测胎儿的生长并降低产前发病率和死亡率。但是，问题在于超声估算胎儿体重受人口差异，对超声医师的严格操作要求以及在资源贫乏地区难以获得超声的影响。错误的估计可能导致围产期不良结果。这项研究的目的是在没有超声检查的情况下以一定的准确性预测不同胎龄下的胎儿体重。我们认为，机器学习可以为产科医生提供准确的估计以及传统的临床实践，并且可以为孕妇进行自我监控提供有效的支持工具。我们提出了使用包含4212产时记录的数据集的可靠方法。三次样条函数用于拟合从超声报告中提取的几个关键特征的曲线。训练了许多简单而强大的机器学习算法，并使用真实的测试数据评估了它们的性能。我们还为我们的研究提出了一种新颖的评估绩效指标，称为联合相交（loU）。使用由随机森林，XGBoost和LightGBM算法组成的集成模型，结果令人鼓舞。实验结果表明，分别由集合模型和超声给出的在任何胎龄的胎儿体重的预测范围之间的loU。在我们的研究中应用的基于机器学习的方法能够高精度地预测