Automated Grading of Vesicoureteral Reflux (VUR) Using a Dual-Stream CNN Model with Deep Supervision.

Abstract

Vesicoureteral reflux (VUR) is a urinary system disorder characterized by the abnormal flow of urine from the bladder back into the ureters and kidneys, often leading to renal complications, particularly in children. Accurate grading of VUR, typically determined through voiding cystourethrography (VCUG), is crucial for effective clinical management and treatment planning. This study proposes a novel multi-head convolutional neural network for the automatic grading of VUR from VCUG images. The model employs a dual-stream architecture with a modified ResNet-50 backbone, enabling independent analysis of the left and right urinary tracts. Our approach categorizes VUR into three distinct classes: no reflux, mild to moderate reflux, and severe reflux. The incorporation of deep supervision within the network enhances feature learning and improves the model's ability to detect subtle variations in VUR patterns. Experimental results indicate that the proposed method effectively grades VUR, achieving an average area under the receiver operating characteristic curve of 0.82 and a patient-level accuracy of 0.84. This provides a reliable tool to support clinical decision-making in pediatric cases.