Reddy Vishnu Vardhan Reddy Kanamata, Villordon Michael, Do Quyen N, Xi Yin, Lewis Matthew A, Herrera Christina L, Owen David, Spong Catherine Y, Twickler Diane M, Fei Baowei
The University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.
The University of Texas at Dallas, Center for Imaging and Surgical Innovation, Richardson, Texas, United States.
J Med Imaging (Bellingham). 2025 Mar;12(2):024502. doi: 10.1117/1.JMI.12.2.024502. Epub 2025 Mar 18.
Identifying pregnant patients at high risk of hysterectomy before giving birth informs clinical management and improves outcomes. We aim to develop machine learning models to predict hysterectomy in pregnant women with placenta accreta spectrum (PAS).
We developed five machine learning models using information from magnetic resonance images and combined them with topographic maps and radiomic features to predict hysterectomy. The models were trained, optimized, and evaluated on data from 241 patients, in groups of 157, 24, and 60 for training, validation, and testing, respectively.
We assessed the models individually as well as using an ensemble approach. When these models are combined, the ensembled model produced the best performance and achieved an area under the curve of 0.90, a sensitivity of 90.0%, and a specificity of 90.0% for predicting hysterectomy.
Various machine learning models were developed to predict hysterectomy in pregnant women with PAS, which may have potential clinical applications to help improve patient management.
在分娩前识别有子宫切除高风险的孕妇，可为临床管理提供信息并改善治疗结果。我们旨在开发机器学习模型，以预测患有胎盘植入谱系障碍（PAS）的孕妇的子宫切除情况。
我们利用磁共振图像信息开发了五个机器学习模型，并将它们与地形图和放射组学特征相结合，以预测子宫切除情况。这些模型在241例患者的数据上进行训练、优化和评估，分别以157例、24例和60例分为训练组、验证组和测试组。
我们分别对模型进行了评估，并采用了集成方法。当这些模型结合使用时，集成模型表现最佳，预测子宫切除的曲线下面积为0.90，灵敏度为90.0%，特异性为90.0%。
我们开发了多种机器学习模型来预测患有PAS的孕妇的子宫切除情况，这可能具有潜在的临床应用价值，有助于改善患者管理。
