Abdelgadir Elhabeeb Selma Mohammed, Mahmoud Ali Sulafa Hassan, Ahmed Elkhidir Babikir Marwa Mohamed, Abdalla Mohammed Fatima Siddig, Mahmoud Ali Salma Hassan, Abd Elfrag Mohamed Nihal Ahmed, Abdalla Elsheikh Nihal Eltayeb
Obstetrics and Gynecology, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Surgical Oncology, Prince Faisal Oncology Center, Buraydah, SAU.
Cureus. 2025 May 7;17(5):e83655. doi: 10.7759/cureus.83655. eCollection 2025 May.
Accurate prediction of the mode of delivery is critical for optimizing maternal and neonatal outcomes and reducing unnecessary cesarean sections. In recent years, AI has emerged as a promising tool for enhancing obstetric decision-making. This systematic review aimed to evaluate and synthesize existing evidence on AI models developed for predicting the mode of delivery, comparing their performance and clinical applicability across diverse settings. A comprehensive literature search was conducted to identify studies that developed and/or validated AI-based predictive models for mode of delivery outcomes, including vaginal birth after cesarean, emergent cesarean section during labor, and spontaneous vaginal delivery failure. Seventeen studies meeting inclusion criteria were analyzed, encompassing various AI models such as Random Forest, Gradient Boosting, XGBoost, CatBoost, support vector machines, neural networks, QLattice, and ensemble methods. Key study characteristics, input variables, model performance metrics, validation methods, and findings were systematically extracted and compared. The included studies, conducted across multiple countries and healthcare settings, demonstrated generally good to excellent predictive performance, with area under the curve values. Real-time intrapartum data significantly enhanced model accuracy in several studies. Ensemble models and advanced machine learning techniques outperformed traditional logistic regression in many cases, although simpler models remained competitive when interpretability was prioritized. Common predictive variables included maternal age, parity, BMI, previous cesarean, sonographic findings, and cervical examination data. Model transparency and external validation were highlighted as critical considerations for clinical translation. AI models show substantial potential for improving the prediction of the mode of delivery and supporting obstetric decision-making. Ensemble and real-time dynamic models demonstrated the highest performance. However, challenges remain regarding external validation, model interpretability, and integration into clinical practice.
准确预测分娩方式对于优化母婴结局和减少不必要的剖宫产至关重要。近年来，人工智能已成为增强产科决策的一种有前景的工具。本系统综述旨在评估和综合关于为预测分娩方式而开发的人工智能模型的现有证据，比较它们在不同环境中的性能和临床适用性。进行了全面的文献检索，以识别开发和/或验证基于人工智能的分娩方式结局预测模型的研究，包括剖宫产术后阴道分娩、分娩期间紧急剖宫产和自然阴道分娩失败。对符合纳入标准的17项研究进行了分析，涵盖了各种人工智能模型，如随机森林、梯度提升、XGBoost、CatBoost、支持向量机、神经网络、QLattice和集成方法。系统地提取并比较了关键研究特征、输入变量、模型性能指标、验证方法和研究结果。纳入的研究在多个国家和医疗环境中进行，总体显示出良好到优异的预测性能，曲线下面积值较高。在一些研究中，实时产时数据显著提高了模型的准确性。在许多情况下，集成模型和先进的机器学习技术优于传统逻辑回归，尽管在优先考虑可解释性时，更简单的模型仍然具有竞争力。常见的预测变量包括产妇年龄、产次、体重指数、既往剖宫产史、超声检查结果和宫颈检查数据。模型透明度和外部验证被强调为临床转化的关键考虑因素。人工智能模型在改善分娩方式预测和支持产科决策方面显示出巨大潜力。集成模型和实时动态模型表现出最高的性能。然而，在外部验证、模型可解释性以及融入临床实践方面仍然存在挑战。
