AIMC Topic: Lithotripsy
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.
BMC medical informatics and decision making
Apr 10, 2025
BACKGROUND: The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construc...
CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.
Urolithiasis
Nov 5, 2024
OBJECTIVES: To develop a deep learning (DL) model based on computed tomography (CT) images to predict the success of extracorporeal shock wave lithotripsy (SWL) treatment for patients with ureteral stones larger than 1 cm.
Development and validation of a nomogram to predict impacted ureteral stones via machine learning.
Minerva urology and nephrology
Aug 2, 2024
BACKGROUND: To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.
Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment.
Computers in biology and medicine
Jul 23, 2024
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
Jun 9, 2024
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
Oct 29, 2021
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
Apr 21, 2021
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...
The prospect of machine learning in predicting post-lithotripsy outcomes.
World journal of urology
Jul 27, 2020
Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.
BMC urology
Jul 3, 2020
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
Dec 19, 2019
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...
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