BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive l...
INTRODUCTION: To evaluate the prevalence, predictors, management, and trends for ureteroenteric strictures (UES) after robot-assisted radical cystectomy (RARC).
BACKGROUND: Data-based approaches promise to use the information in cardiovascular signals to diagnose cardiovascular diseases. Considerable effort has been undertaken in the field of pulse-wave analysis to harness this information. However, the inve...
PURPOSE: Cystectomy associated with non-continent ileal diversion is a common surgery in patients with neurogenic bladder. Few data are available, especially for the robotic approach. Our purpose was to compare open cystectomy (OC) and robot-assisted...
OBJECTIVE: To compare standard-of-care two-dimensional MRI acquisitions of the cervical spine with those from a single three-dimensional MRI acquisition, reconstructed using a deep-learning-based reconstruction algorithm. We hypothesized that the imp...
OBJECTIVE: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty...
OBJECTIVE: To compare image quality and diagnostic accuracy of arterial stenosis in low-dose lower-extremity CT angiography (CTA) between adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) algorithm...
BACKGROUND: Complete intracorporal robotic ileal ureteric replacement is challenging. We aimed to present the surgical technique of robotic ileal ureter replacement with extracorporeal ileal segment preparation for long ureteral strictures.
BACKGROUND: Angiographic parameters can facilitate the risk stratification of coronary lesions but remain insufficient in the prediction of future myocardial infarction (MI).