Introduction Assessing male lower urinary tract symptoms (LUTS) due to Benign outlet obstruction (BOO) remains a challenge in urology due to the limitations of conventional diagnostic methods, which are often invasive, time-consuming, and inefficient...
OBJECTIVES: To automatically identify and diagnose bladder outflow obstruction (BOO) and detrusor underactivity (DUA) in male patients with lower urinary tract symptoms through urodynamics exam.
AIMS: The integration of artificial intelligence (AI) into functional urology management must be assessed for its clinical utility, but hopefully will change, perhaps to revolutionize the way LUTD and other conditions are assessed, the aim being to o...
BACKGROUND: Machine learning algorithms as a research tool, including traditional machine learning and deep learning, are increasingly applied to the field of urodynamics. However, no studies have evaluated how to select appropriate algorithm models ...
PURPOSE: To determine baseline factors and surgical procedures associated with clinically meaningful improvement or deterioration of lower urinary tract symptoms (LUTS) after robot-assisted radical prostatectomy (RARP).
Simple prostatectomy is indicated in patients with enlarged glands (>80 ) who present with lower urinary tract symptoms (LUTS) attributed to benign prostatic hyperplasia. Salvage robotic simple prostatectomy (SSP) is defined as simple prostatectomy ...
BACKGROUND: Uroflowmetry remains an important tool for the assessment of patients with lower urinary tract symptoms (LUTS), but accuracy can be limited by within-subject variation of urinary flow rates. Voiding acoustics appear to correlate well with...
PURPOSE: To diagnose lower urinary tract symptoms (LUTS) in a noninvasive manner, we created a prediction model for bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using simple uroflowmetry. In this study, we used deep learning to a...
High-quality patient care depends on the accuracy and efficacy of clinical decision-making, which can be affected by both cognitive bias and the risk of judgment variability, which is called noise. Deep learning algorithms, artificial intelligence, a...
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