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Lithotripsy

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Artificial intelligence in the diagnosis, treatment and prevention of urinary stones.

Current opinion in urology
PURPOSE OF REVIEW: There has a been rapid progress in the use of artificial intelligence in all aspects of healthcare, and in urology, this is particularly astute in the overall management of urolithiasis. This article reviews advances in the use of ...

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...

[Construction of a back propagation neural network model for predicting urosepsis after flexible ureteroscopic lithotripsy].

Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences
OBJECTIVES: To analyze the association of serum heparin-binding protein (HBP) and C-reactive protein (CRP) levels with urosepsis following flexible ureteroscopic lithotripsy (FURL) and to construct a back propagation neural network prediction model.

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.

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...

Development and validation of a nomogram to predict impacted ureteral stones via machine learning.

Minerva urology and nephrology
BACKGROUND: To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.

CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.

Urolithiasis
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 of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.

BMC medical informatics and decision making
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...