We aimed to develop machine learning(ML) algorithms to evaluate complications of flexible ureteroscopy and laser lithotripsy(fURSL), providing a valid predictive model. 15 ML algorithms were trained on a large number fURSL data from > 6500 patients from the international FLEXOR database. fURSL complications included pelvicalyceal system(PCS) bleeding, ureteric/PCS injury, fever and sepsis. Pre-treatment characteristics served as input for ML training and testing. Correlation and logistic regression analysis were carried out by a multi-task neural network, while explainable AI was used for the predictive model. ML algorithms performed excellently. For intraoperative PCS bleeding, Extra Tree Classifier achieved the best accuracy at 95.03% (precision 80.99%), and greatest correlation with stone diameter(0.21) and residual fragments(0.26). PCS injury was best predicted by RandomForest (accuracy 97.72%, precision 63.50%). XGBoost performed best for ureteric injury (accuracy 96.88%, precision 60.67%). Both demonstrated moderate correlation with preoperative characteristics. Postoperative fever was predicted by Extra Tree Classifier with 91.34% accuracy (precision 58.20%). Cat Boost Classifier predicted postoperative sepsis with 99.15% accuracy (precision 66.38%), and the best overall performance. At logistic regression, postoperative fever/sepsis positively correlated with preoperative urine culture(p = 0.001). ML represents a powerful tool for automatic prediction of outcomes. Our study showed promises in algorithms training and validation on a very large database of patients treated for urolithiasis, with excellent accuracy for prediction of complications. With further research, reliable predictive nomograms could be created based on ML analysis, to serve as aid to urologists and patients in the decision making and treatment planning process.
Urolithiasis. 2025 May 14*** epublish ***
Carlotta Nedbal, Vineet Gauhar, Sairam Adithya, Pietro Tramanzoli, Nithesh Naik, Shilpa Gite, Het Sevalia, Daniele Castellani, Frédéric Panthier, Jeremy Y C Teoh, Ben H Chew, Khi Yung Fong, Mohammed Boulmani, Nariman Gadzhiev, Thomas R W Herrmann, Olivier Traxer, Bhaskar K Somani
Polytechnic University Le Marche, Ancona, Italy. ., Ng Teng Fong General Hospital, Urology, Singapore, Singapore., Symbiosis Institute of Technology, Engineering, Pune, India., Polytechnic University Le Marche, Ancona, Italy., Manipal Academy of Higher Education, Engineering, Manipal, India., Azienda Ospedaliero-Universitaria Ospedali Riuniti Di Ancona, Polytechnic University Le Marche, Ancona, Italy., GRC Urolithiasis No. 20, Sorbonne University, Tenon Hospital, Paris, France., Urology, The Chinese University of Hong Kong, Hong Kong, China., Urology, University of British Columbia, Vancouver, Canada., Urology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Boston Scientific - Urology and Pelvic Health, Paris, France., Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russia., Kantonspital Frauenfeld, Spital Thurgau AG, Frauenfeld, Switzerland., Sorbonne University GRC Urolithiasis No. 20, Tenon Hospital, Paris, France., University Hospital Southampton NHS Foundation Trust, Southampton, UK.