We performed a systematic review and meta-analysis to investigate the use of machine learning techniques for predicting stone-free rates following Shockwave Lithotripsy (SWL). Eight papers (3264 patients) were included. Two studies used decision-tree...
OBJECTIVE: To improve hypospadias classification system, we hereby, show the use of machine learning/image recognition to increase objectivity of hypospadias recognition and classification. Hypospadias anatomical variables such as meatal location, qu...
OBJECTIVE: To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade.
OBJECTIVE: To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis.
OBJECTIVES: To present a comprehensive report regarding our experience with single-port robotic surgery in our first 100 consecutive patients. We describe the diversity of procedures that can be performed with this platform as well as the challenges ...
OBJECTIVES: To demonstrate the utility of a natural language processing (NLP) algorithm for mining kidney stone composition in a large-scale electronic health records (EHR) repository.
OBJECTIVES: To evaluate the efficacy and functionality of robotic telerounding among Korean patients using the RP-7 robot system and a questionnaire survey comparing the results of robotic telerounding and standard rounding in Korean patients.