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Construction of a Precision Ultrasound Diagnostic System for Bladder Tumors Based on Deep Learning: A Multicenter, Ambispective Cohort Study

Construction of a Precision Ultrasound Diagnostic System for Bladder Tumors Based on Deep Learning: A Multicenter, Ambispective Cohort Study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600119679
Enrollment
Unknown
Registered
2026-03-02
Start date
2025-10-24
Completion date
Unknown
Last updated
2026-03-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

urothelial carcinoma of the bladder

Interventions

Observation group:none

Sponsors

Peking University First Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Abdominal ultrasound suspected bladder mass (>=18 years old); 2. Those planned for bladder tumor surgical treatment.

Exclusion criteria

Exclusion criteria: 1. Age > 85 years; 2. Unable to complete abdominal and intra-cavitary ultrasound examination (e.g., uncooperative patients, poor image quality); 3. Underwent bladder tumor surgery, radiotherapy, chemotherapy, or other systemic treatments within the past 3 months; 4. Presence of iatrogenic implants in the bladder, such as D-J stents or catheters; 5. Did not undergo bladder tumor surgery within 2 weeks after the ultrasound examination; 6. Non-urothelial carcinoma or unclear pathological diagnosis.

Design outcomes

Primary

MeasureTime frame
Area Under the Curve (AUC);

Countries

China

Contacts

Public ContactZhang Zheng

Peking University First Hospital

doczhz@aliyun.com+86 10 8357260

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Mar 14, 2026