Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only ...
BACKGROUND: Iatrogenic ureteral injury is a serious complication of abdominopelvic surgery. Identifying the ureters intraoperatively is essential to avoid iatrogenic ureteral injury. We developed a model that may minimize this complication.
This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans wit...
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.
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...
OBJECTIVE: To develop dynamic MRU protocol that focuses on the bladder to capture ureteral jets and to automatically estimate frequency and duration of ureteral jets from the dynamic images.
Artificial intelligence (AI)-driven intraoperative navigation in urological surgery can enhance surgical precision through real-time structure identification and tracking. This study describes a novel AI solution that enables real-time fluorescence-l...
We aimed to develop machine learning(ML) algorithms to evaluate complications of flexible ureteroscopy and laser lithotripsy(fURSL), providing a valid predictive model. 15 ML algorithms were trained on a large number fURSL data from > 6500 patients f...