Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study
Recruiting
Observational Study
Deep Learning
Ultrasound
Bladder Cancer
No Study Drug
Researchers observe your health over time — no experimental treatment is given.
At a Glance
- Age
- 18 – 85
- Sex
- Any
- Study type
- Observational
- Participants needed
- 400 (estimated)
- Sponsor
- Peking University First Hospital · Other
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About This Trial
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.
Trial Locations
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Eligibility Criteria
Inclusion Criteria:① Suspected bladder mass detected by abdominal ultrasound (age ≥18 years);② Patients scheduled for surgical treatment of bladder tumors.
Exclusion Criteria:
* Age \>85 years;
* Patients unable to undergo abdominal/transrectal ultrasound (e.g., uncooperative individuals, techn…
Contacts