TL;DR
Overall, these findings suggest that a machine learning–based approach may provide complementary individualized risk stratification for postoperative stone-free status after RIRS when used alongside established clinical judgment and existing scoring systems.
SHAP analysis revealed that stone burden, preoperative serum creatinine, preoperative white blood cell count, stone complexity, and hydronephrosis were the most critical predictors of SFS.
Difficult ureter (DU) is a challenging condition that prevents successful access to the upper urinary tract during retrograde ureteroscopic lithotripsy (URS). The present study aimed to identify clinical risk factors for DU and to evaluate the predictive performance of machine learning–based models in patients undergoi...
Retrograde intrarenal surgery (RIRS) is widely performed for renal calculi; however, stone-free outcomes vary according to the stone burden and intrarenal anatomy. The RIRS scoring system was developed to objectively predict the stone-free rate (SFR) using preoperative computed tomography (CT) parameters. This st...
An interpretable machine learning model incorporating clinical and radiological features to predict the risk of adverse outcomes in patients with lumbar disc herniation (LDH) following unilateral biportal endoscopic (UBE) surgery was developed and validated.
The developed model accurately predicts the risk of ureteral stricture using perioperative clinical variables and may assist clinicians in surgical planning and postoperative management for patients with impacted ureteral stones.
This model provides a reference for surgical risk stratification, which helps establish personalized postoperative monitoring strategies and demonstrates an interpretable machine learning model to effectively predict POF following fURL.
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