This study aims to develop an ultrasound image-based deep learning system to enable automatic segmentation, T-staging, and pathological grading prediction of bladder tumors. It seeks to enhance the objectivity, accuracy, and efficiency of bladder cancer diagnosis, reduce reliance on physician experience, and provide support for precision medicine and resource optimization.
Study Type
OBSERVATIONAL
Enrollment
400
observational diagnostic model development
Department of Urology, Peking University First Hospital
Beijing, China
RECRUITINGOverall Diagnostic Accuracy
Time frame: From may 2025 to may 2027
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