用机器学习分析膀胱造影图像，客观判断尿液反流严重程度。
Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
- 基于九种图像特征训练六类模型，识别肾盏变形模式。
- 所有模型零误判，对不同分级的细微差异敏感度高。
- 适合临床辅助诊断，提升尿液反流评估一致性。
尿液反流（VUR）传统上依赖主观分级系统，导致诊断差异。本研究探索机器学习在分析排尿性膀胱尿道造影（VCUG）图像中的应用，以提高诊断一致性。共审查113张VCUG图像，并由专家评定VUR严重程度。选取九种图像特征，训练逻辑回归、决策树、梯度提升、神经网络及随机梯度下降等六类预测模型，采用留一法交叉验证评估。分析发现肾盏变形模式是高分级VUR的关键指标。所有模型均实现准确分类，无假阳性或假阴性。显著的曲线下面积（AUC）值证实模型对不同等级特征的细微图像模式具有高度敏感性。结果表明，机器学习可提供一种客观、标准化的替代方案，用于当前主观性强的VUR评估。肾盏变形被确认为严重病例的强预测因子。未来研究应扩大数据集，优化成像特征，提升模型泛化能力，以支持更广泛的临床应用。
原文摘要 · Abstract (English)
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.
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