OBJECTIVE: To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis.
BACKGROUND: Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinicians have limited time to read such large amounts of free-text and are at risk of information overload ...
OBJECTIVE: To explore the potential value of utilizing a commercially available cloud-based machine learning platform to predict surgical intervention in infants with prenatal hydronephrosis (HN).
PURPOSE: We sought to define features that describe the dynamic information in diuresis renograms for the early detection of clinically significant hydronephrosis caused by ureteropelvic junction obstruction.
BACKGROUND: Utilization of the robotic approach to pyeloplasty continues to grow in the field of pediatric urology. Adoption in the infant population has perhaps been the slowest because of the limited operative domain and relatively large instrument...