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Related Experiment Video

Updated: Apr 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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

Carlotta Nedbal1,2,3, Vineet Gauhar3,4, Maria Florencia Frascheri5

  • 1Polytechnic University Le Marche, Ancona, Italy.

Journal of Endourology
|April 15, 2026
PubMed

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IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
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Summary
This summary is machine-generated.

Machine learning models accurately predict pediatric flexible ureteroscopy outcomes. Explainable AI enhances interpretability, aiding personalized surgical risk assessment and treatment planning for better patient care.

Area of Science:

  • Pediatric Urology
  • Artificial Intelligence in Medicine
  • Surgical Outcomes Research

Background:

  • Flexible ureteroscopy (fURSL) is a common procedure for pediatric urolithiasis.
  • Predicting postoperative complications is crucial for individualized risk assessment and surgical planning.
  • Current methods for risk stratification may not fully capture the complexity of patient-specific factors.

Purpose of the Study:

  • To evaluate machine learning (ML) algorithms combined with explainable artificial intelligence (XAI) for predicting outcomes after pediatric fURSL.
  • To identify key preoperative predictors of adverse postoperative events.
  • To enhance individualized surgical risk assessment and treatment planning.

Main Methods:

  • Retrospective analysis of 391 pediatric patients undergoing fURSL for urolithiasis.
Keywords:
artificial intelligencemachine learningpredictionstone-free rate

Related Experiment Videos

Last Updated: Apr 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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  • Training 15 ML models and a multitask artificial neural network (ANN) to predict fever, hematuria, sepsis, residual fragments (RF), and reintervention.
  • Utilizing SHapley Additive exPlanations (XAI) and decision trees for model interpretability.

    • Main Results:

    • Ensemble ML models achieved high accuracy, with Gradient Boosting predicting fever (92.4%), Extra Trees predicting hematuria (91.1%), and XGBoost predicting sepsis (96.0%).
    • Key predictors included preoperative infections, stone burden, operative duration, and anatomical anomalies.
    • XAI techniques provided transparent and clinically interpretable insights, aligning with medical reasoning.

    Conclusions:

    • ML models demonstrate significant accuracy in predicting adverse postoperative outcomes in pediatric ureteroscopy.
    • Integration with XAI improves model interpretability, supporting clinical decision-making and personalized treatment strategies.
    • Further prospective validation is necessary to develop robust and generalizable predictive tools for clinical application.