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April 17, 2026Journal of Endourology0 citations

Predicting Postoperative Outcomes in Pediatric Ureteroscopy Using Machine Learning and Explainable AI—EAU Endourology Vision-AI Study

CNCarlotta Nedbal VGVineet GauharEuropean Association of Urology MFM.F. FrascheriUniversity Hospital Southampton NHS Foundation Trust

Key Points

The aim is to predict adverse postoperative outcomes in pediatric ureteroscopy using machine learning and explainable AI.
Applied machine learning models to postoperative outcome data
Utilized ensemble methods for improved performance
Implemented explainable AI to enhance interpretability
ML models achieved high accuracy in predictions
Ensemble methods outperformed other techniques
Integration with XAI supported better clinical decision-making

Abstract

ML models demonstrated high accuracy in predicting adverse postoperative outcomes in pediatric ureteroscopy, with ensemble methods showing the best performance. Integration with XAI enhanced interpretability, supporting clinical decision-making. These findings underscore the potential of ML and XAI to inform personalized treatment strategies, though further prospective validation is needed to develop robust, generalizable predictive tools.

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