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Published on: April 4, 2025
The future of pediatric vesicoureteral reflux management
Nicolas Kalfa1, Yannis Bonnin2, Benoit Tessier3
1Department of Pediatric Surgery and Urology, Lapeyronie University Hospital, Montpellier, France; UMR 1302 Desbrest Institute of Epidemiology and Public Health, INSERM, Montpellier, France; National Reference Center for Rare Disease of Genital Development DEVGEN, University Hospital, Montpellier, France; Division of Pediatric Surgery, Department of Pediatrics, University Hospital of Geneva, and University Center of Pediatric Surgery of Western Switzerland, University of Geneva, Geneva, Switzerland.
Insights
Artificial intelligence (AI) and immunomodulation show promise for personalized pediatric vesicoureteral reflux (VUR) management. Further validation is needed for these emerging tools to improve risk stratification and prevent renal damage in children with VUR.
Area of Science:
- Pediatric Urology
- Medical Technology
- Immunology
Background:
- Vesicoureteral reflux (VUR) is common in children, with ongoing challenges in risk stratification, imaging, and preventing renal damage.
- Emerging technologies like AI and immunomodulation offer potential solutions to these management uncertainties.
Purpose of the Study:
- To provide a forward-looking overview of recent advances in AI and immunomodulation for pediatric VUR.
- To explore how these technologies may shape future management strategies.
Main Methods:
- A literature review of PubMed (2000-2025) focusing on AI, immunomodulation, and vaccination for VUR and UTIs in children.
- Inclusion criteria emphasized relevance to pediatric VUR, novelty, clinical implications, and clinical evaluation for AI algorithms.
Main Results:
- AI models demonstrate potential in clinical decision support for VUR, including predicting diagnostic needs, grading severity, and estimating infection risk.
- Immunomodulatory and immunization strategies aim to reduce infection and inflammation, but pediatric-specific data and clinical applicability are limited.
- Current AI evidence is mostly retrospective, requiring prospective validation; immunomodulatory approaches need more pediatric research.
Conclusions:
- AI and immunologically targeted strategies are complementary, emerging approaches for personalized pediatric VUR management.
- Both AI and immunomodulation are currently exploratory tools.
- Clinical impact hinges on further validation and pediatric-specific studies.
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.
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