Latest AI and machine learning research in urology for healthcare professionals.
Acute kidney injury (AKI) is a complex syndrome with a paucity of therapeutic development. One aspec...
To evaluate the differences in baseline chronic kidney disease (CKD) status in correlations between...
To evaluate the feasibility and intraoperative technical parameters of the new robot-assisted surgi...
BACKGROUND: Retzius-sparing robot-assisted radical prostatectomy (RARP) is not yet universally accep...
To compare outcomes of robot-assisted partial nephrectomy (RAPN) and percutaneous tumor ablation (P...
PURPOSE: This paper aims to provide an overview of the possibility regarding the artificial intellig...
To maintain a surgeon's concentration, reduce fatigue, and train young surgeons, surgical procedures...
As the growing popularity of robotic-assisted laparoscopic procedures for the treatment of renal can...
BACKGROUND: Ipsilateral synchronous renal and ureteric tumor is uncommon. Nephron sparing surgery is...
To test the hypothesis of an association between the American Society of Anesthesiologists (ASA) phy...
INTRODUCTION: The clinical management of pT3a pathologic-upstaged renal cell carcinoma (RCC) patient...
OBJECTIVES: To compare the diagnostic performance of a novel deep learning (DL) method based on T2-w...
BACKGROUND: Urinary incontinence is a common postoperative complication of radical prostatectomy (RP...
In this study, an inter-fraction organ deformation simulation framework for the locally advanced cer...
The benefits of robot-assisted laparoscopic surgery (RALS) for rectal cancer remain controversial. O...
The use of robotic surgery has increased exponentially in the United States. Despite this uptick in ...
PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric mag...
Laparoscopic nephroureterectomy (LNU) has become popular in treating upper urinary tract urothelial...
Effective learning and modelling of spatial and semantic relations between image regions in various ...
In this work we present a framework for robust deep learning-based VMAT forward dose calculations fo...