Machine learning-based prediction of Clavien-Dindo ≥ II complications after ureteroscopy.

Machine learning for predicting stone-free rate after retrograde intrarenal surgery: Comparison with the T.O.HO. score

Application of Machine Learning to Predict 1-Year Weight Loss Outcomes After Laparoscopic Sleeve Gastrectomy.

Clinical risk factors and machine learning-assisted interpretation of ureteral access sheath insertion failure: a multicenter study and meta-analysis

Background In patients undergoing retrograde ureteroscopic lithotripsy, the failure rate of ureteral access sheath insertion is approximately 10%. Temporary double-J stenting with delayed secondary intervention is usually required when insertion fails. However, such cases cannot be reliably identified based on routine...

Artificial intelligence-based prediction of stone-free status in patients undergoing mini-percutaneous nephrolithotomy without retrograde insertion of a ureteral catheter

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.

A Modified Clavien-Dindo Framework and a Heatmap Analysis to Identify Predictors of Complications After Endoscopic Enucleation of the Prostate.

A standardized framework for reporting complications following endoscopic enucleation of the prostate is proposed using a modified Clavien-Dindo (CD) classification, and predictors of postoperative complications following HoLEP are identified.

Development and validation of an interpretable machine learning model for predicting postoperative fever after flexible ureteroscopic lithotripsy: a single-center retrospective cohort study

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.