Actively Recruiting
Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study
Led by Peking University First Hospital · Updated on 2025-08-17
400
Participants Needed
1
Research Sites
52 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Suspected bladder mass detected by abdominal ultrasound in patients aged 18 years or older
- Patients scheduled for surgical treatment of bladder tumors
You will not qualify if you...
- Age over 85 years
- Unable to undergo abdominal or transrectal ultrasound (e.g., uncooperative or poor image quality)
- History of bladder tumor surgery, radiotherapy, chemotherapy, or systemic therapy within 3 months
- Presence of indwelling medical devices such as double-J ureteral stents or urinary catheters
- Failure to undergo bladder tumor surgery within 2 weeks after ultrasound
- Diagnosis of non-urothelial carcinoma or pathologically unconfirmed tumor type
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Department of Urology, Peking University First Hospital
Beijing, China, 100034
Actively Recruiting
Research Team
Z
Zheng Zhang
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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