Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux

Abstract

Vesicoureteral reflux (VR) is a critical condition in pediatric urology that requires precise diagnosis for effective treatment. Voiding cystourethrograms are the gold standard for diagnosing VR, but traditional grading methods can be subjective. This study discusses the implementation of ML models to automate the grading of VR, enhancing diagnostic precision. The goal is to increase diagnostic precision. Various machine learning models categorize VR grades (Grade 1 to Grade 3) and are evaluated using performance metrics and confusion matrices. Study datasets come from internet repositories with repository names and accession numbers. Machine learning models performed well across several measures. These models classified grades well individually and collectively. In contrast, the Constant model performed poorly across all criteria, suggesting its inability to categorize VR grades reliably. With the most excellent average performance ratings, the large diagonal numbers of the matrices show that the models are regularly predicted effectively. Our findings suggest that integrating machine learning into diagnostic workflows can significantly improve clinical decision-making and patient outcomes in pediatric urology.