Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency

Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, leading to variability in diagnosis. This study explores the potential of machine learning to enhance diagnostic accuracy by analysing voiding cystourethrogram (VCUG) images. The objective is to develop predictive models that provide an objective and consistent approach to VUR classification. A total of 113 VCUG images were reviewed, with experts grading them based on VUR severity. Nine distinct image features were selected to build six predictive models, which were evaluated using 'leave-one-out' cross-validation. The analysis identified renal calyces’ deformation patterns as key indicators of high-grade VUR. The models—Logistic Regression, Tree, Gradient Boosting, Neural Network, and Stochastic Gradient Descent—achieved precise classifications with no false positives or negatives. High sensitivity to subtle patterns characteristic of different VUR grades was confirmed by substantial Area Under the Curve (AUC) values. This study demonstrates that machine learning can address the limitations of subjective VUR assessments, offering a more reliable and standardized grading system. The findings highlight the significance of renal calyces’ deformation as a predictor of severe VUR cases. Future research should focus on refining methodologies, exploring additional image features, and expanding the dataset to enhance model accuracy and clinical applicability.