This study aimed to evaluate the performance of machine learning (ML) algorithms integrated with explainable artificial intelligence (XAI) techniques in predicting outcomes following flexible ureteroscopy (fURSL) in children. By identifying significant preoperative predictors, the goal was to improve individualized surgical risk assessment and planning.
A retrospective analysis was conducted on 391 pediatric patients treated with fURSL for urolithiasis across 8 high-volume centers (2017-2021). Preoperative, intraoperative, and postoperative data were collected. 15 ML models were trained to predict five postoperative outcomes: fever, hematuria, sepsis, residual fragments (RF), and reintervention. A multitask artificial neural network (ANN) was also developed. Performance was evaluated using validation accuracy, confusion matrices, and classification reports. SHapley Additive exPlanations values and decision trees were used for model interpretability.
Ensemble models outperformed others, with Gradient Boosting achieving 92.4% validation accuracy in predicting postoperative fever, Extra Trees achieving 91.1% for hematuria, and XGBoost reaching 96.0% for sepsis. Predictors included preoperative infections, stone burden, operative duration, and anatomical anomalies. For RF, Gradient Boost and Random Forest yielded strong results with up to 93.7% accuracy. Reintervention was best predicted by Random Forest, with RF as the strongest predictor. XAI techniques provided transparent, clinically interpretable models that aligned with medical reasoning.
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.
Journal of endourology. 2026 Apr 14 [Epub ahead of print]
Carlotta Nedbal, Vineet Gauhar, Maria Florencia Frascheri, Shilpa Gite, Het Sevalia, Ratan Maurya, Prisha Jaiswal, Khushi Kashyap, Daniele Castellani, Andrea Gregori, Frédéric Panthier, Yiloren Tanidir, Anil Shrestha, Vikram Sridharan, Abhishek Singh, Boyke Soebhali, Mohamed Amine Lakmichi, Saeed Biin Hamri, Nithesh Naik, Bhaskar Kumar Somani
IRCCS San Gerardo dei Tintori, Monza, Italy., Endourology Section, European Association of Urology, Arnhem, The Netherlands., University Hospital Southampton NHS Foundation Trust, Southampton, UK., Department of Engineering, Symbiosis Institute of Technology, Pune, India., Department of Medicine and Surgery, LUM University, Casamassima, Bari, Italy., IRCSS San Gerardo, Monza, Italy., Department of Urology, Marmara University School of Medicine, Istanbul, Turkey., Department of Urology, National Academy of Medical Sciences, Bir Hospital, Kathmandu, Nepal., Department of Urology, Sree Paduka Speciality Hospital, Thillai Nagar, India., Muljibhai Patel Urological Hospital, Nadiad, India., Department of Urology, Abdul Wahab Sjahranie Hospital Medical Faculty, Muliawarman University, Samarinda, Indonesia., Department of Urology, University Hospital Mohammed the VIth of Marrakesh, Marrakesh, Morocco., Department of Surgery, King Abdullah International Medical Research Centre, Riyadh, Saudi Arabia., Department of Engineering, Manipal Academy of Higher Education, Manipal, India.