AIMC Topic: Ureteroscopy

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Preoperative CT imaging and machine learning models for predicting ureteral access sheath placement success in non-stented patients with ureteral calculi: a retrospective cohort study.

World journal of urology
OBJECTIVE: This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of u...

Efficacy and safety of ureteroscopy in children with lower pole renal stones : a machine learning predictive model from the EAU section of endourology.

World journal of urology
INTRODUCTION: The rising incidence of kidney stone disease in children presents growing clinical challenges, particularly in managing lower pole (LP) calculi, which are anatomically difficult to treat. Flexible ureteroscopy with laser lithotripsy (fU...

Intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy: a systematic review from EAU endourology.

World journal of urology
INTRODUCTION: The current systematic review aims to summarize the existing data on intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy in order to assess which particular applications are feasible and have prospects of wi...

Comprehensive analysis of 55,213 stones: understanding common morphological associations advances endoscopic stone recognition and AI integration.

World journal of urology
PURPOSE: To assess the prevalence and associations of urinary stone morphologies, focusing on their relevance for Endoscopic Stone Recognition and improving AI-assisted ESR (AESR) systems.

Development and validation of an explainable machine learning model for predicting sepsis risk following flexible ureteroscopic lithotripsy.

Urolithiasis
Sepsis is a severe complication of flexible ureteroscopic lithotripsy (fURL), a widely used treatment for kidney stones. This study aimed to develop and validate a predictive model based on machine learning (ML) for assessing the risk of sepsis follo...

Machine learning models to predict the zero-fragment rate and lower pole access with FANS during flexible Ureteroscopy-an EAU section of endourology study.

World journal of urology
INTRODUCTION: Suction devices such as flexible and navigable suction ureteral access sheath (FANS) are promising tools to reach the zero-fragment rate (ZFR) after flexible ureteroscopy (FURS) and laser lithotripsy. FANS could especially be useful for...

From laser-on time to lithotripsy duration: improving the prediction of lithotripsy duration with 'Kidney Stone Calculator' using artificial intelligence.

World journal of urology
INTRODUCTION: "Kidney Stone Calculator" (KSC) helps to plan flexible ureteroscopy, providing the stone volume (SV) and an estimated duration of laser lithotripsy (eLD). eLD is calculated from in vitro ablation rates and SV. KSC's accuracy has been de...

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...

Measuring kidney stone volume - practical considerations and current evidence from the EAU endourology section.

Current opinion in urology
PURPOSE OF REVIEW: This narrative review provides an overview of the use, differences, and clinical impact of current methods for kidney stone volume assessment.

Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: Ureteral stents, such as double-J stents, have become indispensable in urologic procedures but are associated with complications like hematuria and pain. While the advancement of artificial intelligence (AI) technology has led to its incr...