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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

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CNCarlotta NedbalVGVineet GauharMFM.F. Frascheri

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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Cite This Study

Nedbal et al. (2026) studied this question.

synapsesocial.com/papers/69e1ce065cdc762e9d8573b8https://doi.org/10.1177/08927790261441731
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