The rising incidence of kidney stone disease in children presents growing clinical challenges, particularly in managing lower pole (LP) calculi, which are anatomically difficult to treat. Flexible ureteroscopy with laser lithotripsy (fURSL) has emerged as a preferred minimally invasive treatment. However, surgical outcomes remain variable, especially in the paediatric LP stone cohort. This study aimed to apply machine learning (ML) techniques to predict surgical outcomes based on preoperative characteristics and identify key predictors of incomplete stone clearance.
A retrospective analysis was conducted on paediatric patients (≤ 16 years) who underwent fURSL between January 2017 and December 2021 across eight tertiary centres. From a multicentre database of 280 patients, 91 with isolated LP stones were selected. Preoperative, intraoperative, and postoperative variables were analysed. Fifteen ML models-including ensemble algorithms and a multitask neural network-were developed to predict LP stone presence and postoperative outcomes. Model performance was evaluated using accuracy, precision, recall, F1-score, and SHAP (SHapley Additive exPlanations) values for interpretability.
LP stones were present in 32.5% of cases and were associated with older age, solitary stones, and higher stone burden. Random Forest outperformed all other models (validation accuracy: 80.95%; F1-score: 76.67%), followed by Gradient Boosting. SHAP analysis identified stone number, total stone burden, age, and operative time as top predictors. LP stones were associated with a higher rate of residual fragments (RF) and lower need for preoperative stenting or ureteral access sheath use. Infectious and bleeding complications were less frequent in the LP group.
fURSL is safe and effective in children with LP stones, though incomplete stone clearance remains a challenge. ML models demonstrated strong predictive performance and could support preoperative risk stratification. Further external validation and prospective studies are warranted to refine predictive tools for clinical use.
World journal of urology. 2025 Nov 20*** epublish ***
Carlotta Nedbal, Vineet Gauhar, Shilpa Gite, Het Sevalia, Ratan Maurya, Prisha Jaiswal, Khushi Kashyap, Andrea Gregori, Francesco Antomarchi, Frédéric Panthier, Yiloren Tanidir, Abhishek Singh, Boyke Soebhali, Hsiang Ying Lee, Steffi Kar Kei Yuen, Ee Jean Lim, Nitesh Naik, Bhaskar Kumar Somani
Polytechnic University Le Marche, Ancona, Italy. ., Endourology Section, European Association of Urology, Arnhem, The Netherlands., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India., IRCCS San Gerardo dei Tintori, Monza, Italy., Department of Urology, Marmara University School of Medicine, Istanbul, Turkey., Muljibhai Patel Urological Hospital, Nadiad, Gujarat, India., Department of Urology, Abdul Wahab Sjahranie Hospital Medical Faculty, Muliawarman University, Samarinda, Indonesia., Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan., Department of Surgery, SH Ho Urology Centre, The Chinese University of Hong Kong, Shatin, Hong Kong., Department of Urology, Singapore General Hospital, Singapore, Singapore., Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 57614, Karnataka, India.