Peer-reviewed article
The future of pediatric vesicoureteral reflux management.
Nicolas Kalfa, Yannis Bonnin, Benoit Tessier, Sarah Garnier, Nesrine Mekhenane et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This review examines emerging technologies that may improve how doctors manage vesicoureteral reflux (VUR), a common condition where urine flows backward from the bladder toward the kidneys in children. The authors review advances in artificial intelligence and immunological approaches that could help predict which children need specific tests, assess infection risk, and prevent kidney damage. While early results are promising, most evidence comes from retrospective studies and needs further testing in children before being used in practice.
Key points
- AI-based models show promise in predicting need for diagnostic imaging, automating VUR grading, estimating recurrent urinary tract infection risk, and predicting need for chemoprophylaxis
- Current AI evidence is largely retrospective and requires prospective validation before clinical use
- Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways linked to renal scarring
- Early experimental and adult clinical data on immunological approaches are encouraging, but pediatric-specific evidence is limited
- Both AI and immunologically targeted strategies are described as exploratory tools requiring further validation through appropriately designed pediatric studies
Limitations
This is a forward-looking review of emerging technologies rather than an evaluation of established interventions. The document does not provide evidence of clinical effectiveness in children with VUR, as both AI and immunomodulation approaches are still largely experimental or have limited pediatric validation.
Abstract
BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.