AIMC Topic: Lithotripsy

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Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment.

Computers in biology and medicine
Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patie...

Predicting the Efficacy of Repeated Shockwave Lithotripsy for Treating Patients with Upper Urinary Tract Calculi Using an Artificial Neural Network Model.

Urology journal
PURPOSE: To establish a prediction model for repeated shockwave lithotripsy (SWL) efficacy to help choose an appropriate treatment plan for patients with a single failed lithotripsy, reducing their treatment burden.

Image Enhancement Model Based on Deep Learning Applied to the Ureteroscopic Diagnosis of Ureteral Stones during Pregnancy.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the image enhancement model based on deep learning on the effect of ureteroscopy with double J tube placement and drainage on ureteral stones during pregnancy. We compare the clinical effect of ureteroscopy with double J tube pl...

Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis.

Urology
We performed a systematic review and meta-analysis to investigate the use of machine learning techniques for predicting stone-free rates following Shockwave Lithotripsy (SWL). Eight papers (3264 patients) were included. Two studies used decision-tree...

Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.

BMC urology
BACKGROUND: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencin...

Endoscopic-assisted robotic pyelolithotomy: a viable treatment option for complex pediatric nephrolithiasis.

Journal of pediatric urology
INTRODUCTION AND OBJECTIVE: Endourological and percutaneous approaches are the standard of care for treatment of pediatric urolithiasis. However, in certain situations, an endoscopic-assisted robotic pyelolithotomy (EARP) can be an acceptable alterna...

A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones.

The Journal of urology
PURPOSE: The aim of this study was to develop and validate a decision support model using a machine learning algorithm to predict treatment success after single session shock wave lithotripsy in ureteral stone cases.

Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy.

Journal of endourology
PURPOSE: To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome var...

Machine learning-based screening of characteristic factors for urinary tract infection following ureteral stone surgery and construction and validation of risk prediction models.

Medicine
Ureteroscopic lithotripsy has emerged as the cornerstone treatment modality for ureteral stones due to its exceptional success rates and minimal complication profiles. Nevertheless, postoperative urinary tract infection (UTI) remains a prevalent and ...