基于机器学习和超声图像评估中国产妇引产时的宫颈成熟度。

An evaluation of cervical maturity for Chinese women with labor induction by machine learning and ultrasound images.

机构信息

School of Computer Science and Engineering, Beihang University, Beijing, 100191, China.

Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China.

出版信息

BMC Pregnancy Childbirth. 2023 Oct 18;23(1):737. doi: 10.1186/s12884-023-06023-4.

DOI:10.1186/s12884-023-06023-4

PMID:37853378

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10583473/

Abstract

BACKGROUND

To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction by adding objective ultrasound data and machine learning models to the existing traditional Bishop method.

METHODS

The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score.

RESULTS

The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 h and the RMSE was 16.98 h. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was < 0.01.

CONCLUSION

In the evaluation of cervical maturity, the results of machine learning method are more objective and significantly accurate compared with the traditional Bishop scoring method. The machine learning method is a better predictor of cervical maturity than the traditional Bishop method.

摘要

背景

通过在现有的传统 Bishop 评分法中加入客观的超声数据和机器学习模型，来评估其对中国产妇行引产时宫颈成熟度评估准确性的改善。

方法

使用 2019 年 12 月至 2021 年 1 月期间在北京大学第三医院检查并分娩的 101 组孕妇的数据对机器学习模型进行训练和测试。模型的输入包括宫颈长度、Bishop 评分、角度、年龄、引产时间、测量时间 (MT)、测量时间至引产时间 (MTILT)、引产方式以及初产妇/经产妇。模型的输出是从引产到临产的预测时间。我们的实验分析了三种机器学习模型（XGBoost、CatBoost 和 RF（随机森林））的有效性。我们以均方根误差 (RMSE) 和平均绝对误差 (MAE) 作为评价模型准确性的标准。在 RMSE 上，使用 t 检验比较机器学习模型与传统 Bishop 评分之间的差异。

结果

Bishop 评分法预测结果的平均绝对误差为 19.45 h，RMSE 为 24.56 h。机器学习模型的预测误差低于 Bishop 评分法。在三种机器学习模型中，预测效果最佳的模型的 MAE 为 13.49 h，RMSE 为 16.98 h。在选择特征后，XGBoost 和 RF 的预测准确性略有提高。在特征选择和人工去除 Bishop 评分后，三个模型的预测准确性略有下降。最佳模型为 XGBoost（p=0.0017）。其他两个模型的 p 值均<0.01。