Latest AI and machine learning research in urology for healthcare professionals.
OBJECTIVES: To investigate how much minimal residual membranous urethral length (mRUL) and maximal u...
BACKGROUND: Nerve-sparing (NS) techniques could potentially increase positive surgical margins after...
OBJECTIVES: To assess the morbidity specific of extended pelvic lymphadenectomy during robot-assiste...
BACKGROUND: We assess whether short-term recovery of urinary incontinence following robot-assisted l...
Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables ...
Liquid crystal (LC)-based sensors have been extensively applied in the detection of chemical and bio...
The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents...
PURPOSE: Retinal vessels reflect alterations related to hypertension and arteriosclerosis in the phy...
PURPOSE: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based...
BACKGROUND: Symptomatic lymphoceles present the most common complication of robot-assisted radical p...
The success of neural networks on medical image segmentation tasks typically relies on large labeled...
BACKGROUND: Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of pa...
INTRODUCTION: Durable techniques that prevent postoperative inguinal hernia (IH) after robot-assiste...
BACKGROUND: Urine cytology is commonly used as a screening test for high-grade urothelial carcinoma ...
Cyclohexane oxidation chemistry was investigated using a near-atmospheric pressure jet-stirred react...
PURPOSE: Few data exist regarding the functional outcomes of robot-assisted radical cystectomy (RARC...
BACKGROUND: Ureteral injury and vaginal fistula are common complications after surgical treatment an...
This paper presents an automatic recognition system for classifying stones belonging to different Ca...
We evaluate the accuracy of an original hybrid segmentation pipeline, combining variational and deep...