Transforming Pediatric Urology With Artificial Intelligence: A Narrative Review of Current Evidence and Practice

TL;DR

Artificial intelligence is improving pediatric urology by enhancing diagnosis, surgery, and monitoring, though challenges like data scarcity remain.

Contribution

This paper reviews current AI applications in pediatric urology and highlights opportunities for future development.

Findings

AI improves hydronephrosis grading and obstruction prediction using deep learning models.

AI reduces subjectivity in diagnosing penile anomalies like hypospadias through image analysis.

Machine learning helps in risk-stratifying vesicoureteral reflux patients for better treatment planning.

Abstract

The use of artificial intelligence (AI) with pediatric urology is reshaping clinical care by enhancing diagnostic accuracy, surgical planning, and postoperative monitoring. AI integration offers significant advancements in managing conditions like hydronephrosis, where deep learning models automate severity grading and predict obstruction more accurately than conventional methods. In penile anomalies, such as hypospadias, AI reduces subjectivity through standardized image analysis, aiding in diagnosis and surgical decision-making. Furthermore, machine learning algorithms assist in risk-stratifying patients with vesicoureteral reflux to optimize antibiotic prophylaxis and predict resolution. Beyond diagnostics, AI enhances surgical precision in procedures like pyeloplasty through 3D simulation and real-time intraoperative navigation. However, widespread adoption is currently limited by challenges such as data scarcity, strict privacy regulations, and the need for ethical legal frameworks regarding patient data. Future developments must focus on creating transparent, interpretable decision support systems that can safely integrate into standard clinical workflows.

Figures