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

Journal: Annals of surgical oncology
Published Date: Mar 6, 2025

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.

Authors

  • Zixing Ye
    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Dongcheng, Beijing, 100000, China.
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Yujiao Sun
    School of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China.
  • Chengqing He
    Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Guanglin He
    Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, China.
  • Zhigang Ji
    Department of Urology, Peking Union Medical College, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China.

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