Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Department of Big Data and Artificial Intelligence, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Int J Gynaecol Obstet. 2024 Oct;167(1):403-412. doi: 10.1002/ijgo.15739. Epub 2024 Jun 20.
This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms.
In this retrospective observational study, data on nulliparous women without contraindications for vaginal delivery with a singleton pregnancy ≥37 weeks and before the onset of labor from September 2020 to September 2021 were divided into a training set and a temporal validation set. Transperineal ultrasound was performed to collect angle of progression, head-perineum distance, subpubic arch angle, and their levator hiatal dimensions. The cervical length was measured via transvaginal ultrasound. The delivery methods were later recorded. Through LASSO regression analysis, indicators that can affect SVD failure were selected. Seven common machine learning algorithms were selected for model training, and the optimal algorithm was selected based on the area under the curve (AUC) to evaluate the effectiveness of the validation model.
Four indicators related to SVD failure were identified through LASSO regression screening: angle of progression, cervical length, subpubic arch angle, and estimated fetal weight. The Gaussian NB algorithm was found to yield the highest AUC (0.82, 95% confidence interval [CI] 0.65-0.98) during model training, and hence it was chosen for verification with the temporal validation set, in which an AUC of 0.79 (95% CI 0.64-0.95) was obtained with accuracy, sensitivity, and specificity rates of 80.9%, 72.7%, and 75.0%, respectively.
The Gaussian NB model showed good predictive effect, proving its potential as a clinical reference for predicting SVD failure of term nulliparous women before actual delivery.
本研究旨在构建并评估基于机器学习算法预测足月初产妇自然分娩失败的模型。
本回顾性观察性研究纳入了 2020 年 9 月至 2021 年 9 月无阴道分娩禁忌、单胎妊娠≥37 周且未临产的初产妇无妊娠并发症的数据，将其分为训练集和时间验证集。经会阴超声采集进展角度、胎头会阴距离、耻骨弓角度及其肛提肌裂孔尺寸；经阴道超声测量宫颈长度。分娩方式后记录。通过 LASSO 回归分析筛选影响 SVD 失败的指标。选择 7 种常见的机器学习算法进行模型训练，根据曲线下面积（AUC）选择最优算法来评估验证模型的有效性。
通过 LASSO 回归筛选出与 SVD 失败相关的 4 个指标：进展角度、宫颈长度、耻骨弓角度和估计胎儿体重。在模型训练过程中，高斯 NB 算法的 AUC 最高（0.82，95%置信区间 [CI] 0.65-0.98），因此选择该算法对时间验证集进行验证，AUC 为 0.79（95%CI 0.64-0.95），准确率、敏感度和特异度分别为 80.9%、72.7%和 75.0%。
高斯 NB 模型具有良好的预测效果，有望成为预测足月初产妇实际分娩前 SVD 失败的临床参考。
