Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem

Abstract

Our machine learning framework significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared to half-time and 30-minute clearance. This aids in the clinical decision making process by offering a tool for earlier detection of severe cases and it has the potential to reduce the number of diuresis renograms required for diagnosis.