Abstract
Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, which introduces variability in diagnosis. This study investigates the use of machine learning to improve diagnostic consistency by analyzing voiding cystourethrogram (VCUG) images. A total of 113 VCUG images were reviewed, with expert grading of VUR severity. Nine image-based features were selected to train six predictive models: Logistic Regression, Decision Tree, Gradient Boosting, Neural Network, and Stochastic Gradient Descent. The models were evaluated using leave-one-out cross-validation. Analysis identified deformation patterns in the renal calyces as key indicators of high-grade VUR. All models achieved accurate classifications with no false positives or negatives. High sensitivity to subtle image patterns characteristic of different VUR grades was confirmed by substantial Area Under the Curve (AUC) values. The results suggest that machine learning can offer an objective and standardized alternative to current subjective VUR assessments. These findings highlight renal calyceal deformation as a strong predictor of severe cases. Future research should aim to expand the dataset, refine imaging features, and improve model generalizability for broader clinical use.
Abstract (translated)
输尿管膀胱反流（Vesicoureteral reflux，简称 VUR）的传统评估方法依赖于主观分级系统，这会导致诊断的一致性较差。本研究探讨了利用机器学习技术分析排尿膀胱造影（Voiding Cystourethrogram，VCUG）图像来提高诊断一致性的可能性。研究团队审查了总共113幅 VCUG 图像，并由专家对其 VUR 的严重程度进行了分级。随后选择了九个基于图像的特征，用于训练六种预测模型：逻辑回归、决策树、梯度提升、神经网络和随机梯度下降。通过留一交叉验证（leave-one-out cross-validation）方法评估这些模型的效果。 分析发现，在肾盏中观察到的变形模式是高级别 VUR 的关键指标之一。所有模型均能够准确分类，且无误报或漏报情况发生。通过对不同级别的VUR特有的细微图像特征具有高度敏感性的确认（通过显著的曲线下面积值即AUC值得出），证实了机器学习方法在识别这些特征上的有效性。 研究结果表明，机器学习可以为当前主观评估 VUR 的方式提供一个客观且标准化的选择方案。此外，该研究表明肾盏变形是严重病例的重要预测因素。未来的研究应该致力于扩大数据集规模、优化成像特征以及改进模型的泛化能力，从而使其在更广泛的临床应用中更具实用性。
URL
https://arxiv.org/abs/2506.11508
