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Evaluate the effectiveness and quality of artificial intelligence applications in predicting outcomes of vesicoureteral reflux (VUR).

February 16, 2026PLOS Digital HealthOpen Access

Predicting vesicoureteral reflux outcomes using artificial intelligence: A critical appraisal using APPRAISE-AI

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Narrative review assesses AI's potential for improving VUR diagnosis and outcomes in children, suggesting need for quality enhancement.

Evaluate the effectiveness and quality of artificial intelligence applications in predicting outcomes of vesicoureteral reflux (VUR).
Identified studies from the AI-PEDURO repository on AI applications in pediatric urology.
Included studies that utilized machine learning methods for predicting VUR-related outcomes.
Reviewed 17 studies for quality using the APPRAISE-AI tool.
AI shows strong potential to improve VUR diagnosis and outcome prediction.
Most studies exhibited low to moderate quality, especially in methodological robustness.
Common AI methods included neural networks, tree-based algorithms, and support vector machines.

Khondker et al. (2026) studied this question.

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