Prediction model for additional procedure requirement in flexible ureterorenoscopy using explainable artificial intelligence.

A significant proportion of patients require additional intervention following flexible ureterorenoscopy (f-URS), a commonly performed procedure in stone surgery. This study aimed to develop machine learning (ML)–based prediction models for additional intervention after f-URS and to enhance their clinical applicability through explainable artificial intelligence (XAI) methods. A retrospective analysis was performed on 656 patients who underwent f-URS between 2015 and 2025. Demographic, clinical, anatomical, and operative variables were collected. Feature selection was conducted using Boruta, LASSO, and ElasticNet, and fourteen ML algorithms, along with ensemble approaches, were evaluated. Model explainability was assessed using SHAP, LIME, model-based importance, and permutation importance analyses.Additional intervention was required in 180 patients (27.4%), including repeat f-URS (n = 94), ureteroscopy (n = 61), extracorporeal shock wave lithotripsy (n = 22), and percutaneous nephrolithotomy (n = 3). The ureteropelvic junction–pelvis angle (UPJ–PA) emerged as the strongest predictor of additional intervention, with intervention rates of 84.3% below the 110° threshold compared with 2.8% above it (OR: 29.6; p < 0.001). Logistic regression demonstrated excellent discriminative performance (AUC = 0.987), while ridge classifier showed high clinical sensitivity with a low false-negative rate. Comparative explainability analyses consistently identified UPJ–PA as the dominant predictor across all methods (normalized importance score: 1.000), followed by access sheath diameter and use of flexible and navigable suction ureteral access sheaths (FANS-UAS). Robustness analysis confirmed the stability of UPJ–PA against measurement variability.ML and explainable XAI provide accurate, transparent, and clinically meaningful predictions for additional intervention after f-URS. A UPJ–PA below 110° represents a critical anatomical risk marker across all renal stone locations, while operative factors such as access sheath diameter and FANS-UAS use further influence clinical outcomes. These findings support the role of AI-based decision support systems in improving patient selection and surgical planning in routine f-URS practice.