OBJECTIVE: This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of u...
OBJECTIVE: To develop, validate, and compare a Traditional Multivariable Logistic Regression model with a Machine Learning-based LASSO Regression Model for predicting significant renal function recovery in adult patients undergoing surgical repair fo...
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...
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.
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...
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.
Journal of imaging informatics in medicine
Oct 31, 2024
Early screening is crucial in reducing the mortality of colorectal cancer (CRC). Current screening methods, including fecal occult blood tests (FOBT) and colonoscopy, are primarily limited by low patient compliance and the invasive nature of the proc...
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...
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