A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.

We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Data from pediatric patients who underwent ureteroscopy for stone treatment by a single experienced surgeon, between 2010 and 2023 in Southampton General Hospital, were retrospectively collected. Fifteen ML classification algorithms were used to investigate correlations between preoperative characteristics and postoperative outcomes: primary stone-free status (SFS, defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments >2 mm at Xray kidney-ureters-bladder (XR KUB) or ultrasound kidney-ureters-bladder (US KUB) at 3 months follow-up) and complications. For the task of complication and stone status, an ensemble model was made out of Bagging classifier, Extra Trees classifier, and linear discriminant analysis. Also, a multitask neural network was constructed for the simultaneous prediction of all postoperative characteristics. Finally, explainable artificial intelligence techniques were used to explain the prediction made by the best models. The ensemble model produced the highest accuracy (90%) in predicting SFS, finding correlation with overall stone size (-0.205), presence of multiple stones (-0.127), and preoperative stenting (-0.102). Complications were predicted by Synthetic Minority Oversampling Technique (SMOTE) oversampled dataset (93.3% accuracy) with relation to preoperative positive urine culture (-0.060) a1nd SFS (0.003). Training ML for the multitask model, accuracies of 83.3% and 80% were respectively reached. ML has a great potential of assisting health care research, with possibilities to investigate dataset at a higher level. With the aid of this intelligent tool, urologists can implement their practice and develop new strategies for outcome prediction and patient counseling and informed shared decision-making. Our model reached an excellent accuracy in predicting SFS and complications in the pediatric population, leading the way to the validation of patient-specific predictive tools.