Leveraging Deep Learning in Real-Time Intelligent Bladder Tumor Detection During Cystoscopy: A Diagnostic Study.

ABSTRACT:

Background

Accurate detection of bladder lesions during cystoscopy is crucial for early tumor diagnosis and recurrence monitoring. However, conventional visual inspection methods have low and inconsistent detection rates. This study aimed to evaluate the effectiveness of the HRNetV2 deep learning model for intelligent bladder lesion detection, focusing on its performance at different image resolutions.

Patients and methods

We recruited 94 patients undergoing cystoscopy or transurethral resection of bladder tumor and collected 102 white-light cystoscopy videos between July 2022 and July 2023. Frames containing suspected bladder lesions were manually annotated. HRNetV2, a semantic segmentation model, was used to identify tumor-related morphological features. Images were catego

SUBMITTER: Ye Z 

PROVIDER: S-EPMC11976823 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature