OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).
OBJECTIVE: To predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesi...
OBJECTIVE: To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.
Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and outcome predictions are constantly evolving. We performed a narrative review utilizing PubMed electroni...
OBJECTIVE: To improve diagnosis of interstitial cystitis (IC)/bladder pain syndrome(IC) we hereby developed an improved IC risk classification using machine learning algorithms.
OBJECTIVE: To evaluate the outcomes of children with vesicoureteral reflux (VUR) and obstructive megaureter (OM) utilizing various laparoscopic and robot-assisted approaches.
OBJECTIVE: To present the patient-reported quality of life (QoL) outcomes from a prospective, randomized controlled trial comparing the use of pelvic floor muscle training (PFMT) and duloxetine after robot-assisted radical prostatectomy (RARP).
OBJECTIVE: To assess the reliability, agreement with provider measurement, and patient preferences regarding patient self-measurement of postvoid residual bladder volume (PVR). PVR measurement in the nonhealthcare setting is a valuable opportunity fo...