Dynamic Multi-Image Weighting for Automated Detection and Diagnosis of Abnormal Urinary Tract on Voiding Cystourethrography with a Deep Learning System: A Retrospective, Large-Scale, Multicenter Study.

ABSTRACT: We aimed to develop a voiding cystourethrography (VCUG) diagnostic artificial intelligence model (VCUG-DAM), which relies on a novel architecture to automatically segment and diagnose the bladder, urethra, and ureters using a single VCUG image, while dynamically assessing the relative importance of each image. A total of 7,899 VCUG images from 1,660 patients across 15 Chinese hospitals were collected between 2021 and 2023. In stage 1, we assessed the performances of the VCUG-DAM model. The patient-level area under the curve (AUC) of VCUG-DAM was 0.8772, 0.7752, 0.9443, and 0.9342 for bladder, urethral, left, and right vesicoureteral reflux (VUR), respectively. In stage 2, we explored whether the VCUG-DAM model could improve the diagnostic ability of clinicians. VCUG-DAM improved the clinic

SUBMITTER: Wu M 

PROVIDER: S-EPMC12280329 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature