Deep LearningBCA-AI-USNCT07111364
Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study
An observational study of Deep Learning and Ultrasound, run by Peking University First Hospital at 1 site in China.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Deep Learning, Ultrasound, Bladder Cancer
- What is being tested?
- observational diagnostic model development
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 27 May 2025to 31 May 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study consecutively enrolled patients with suspected bladder tumors prospectively registered at the Department of Urology, Peking University First Hospital and Shanxi Province Cancer Hospital between May 2025 and May 2027.
Who may be able to join
None listed.
Who cannot join
- Age >85 years;
- Patients unable to undergo abdominal/transrectal ultrasound (e.g., uncooperative individuals, technically inadequate images);
- History of bladder tumor surgery, radiotherapy, chemotherapy, or systemic therapy within 3 months; ④ Patients with indwelling medical devices (e.g., double-J ureteral stents, urinary catheters);
- Failure to undergo bladder tumor surgery within 2 weeks post-ultrasound; ⑥ Non-urothelial carcinoma or pathologically unconfirmed diagnoses.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Department of Urology, Peking University First Hospital
Beijing, 100034
Zheng Zhang, doczhz@aliyun.com
About this study
Official title: Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound
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
- Deep Learning, Ultrasound, Bladder Cancer
- Being tested
- observational diagnostic model development
- Medical subject headings
- Urinary Bladder Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
observational diagnostic model developmentOther
observational diagnostic model development
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall Diagnostic Accuracy
Measured: From may 2025 to may 2027
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Aug 2025
- Study start27 May 2025
- Primary completion1 May 2026 (estimated)
- Study completion31 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University First Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07111364
- Sponsor's protocol ID
- BCA-AI-US
- Acronym
- BCA-AI-US
- Status last verified
- May 2025
- First submitted
- 1 Aug 2025
- Last updated
- 17 Aug 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 17 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound
- ClinicalTrials.gov ID
- NCT07111364
- Acronym / sponsor ID
- BCA-AI-US / BCA-AI-US
- Conditions
- Deep Learning, Ultrasound, Bladder Cancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Peking University First HospitalUniversity, hospital or other
- Study start
- 27 May 2025 (actual)
- Primary completion
- 1 May 2026 (estimated)
- Study completion
- 31 May 2026 (estimated)
- First posted
- 8 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study consecutively enrolled patients with suspected bladder tumors prospectively registered at the Department of Urology, Peking University First Hospital and Shanxi Province Cancer Hospital between May 2025 and May 2027.
Key inclusion criteria (0 listed)
Key exclusion criteria (4 listed)
- Age >85 years;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| observational diagnostic model developmentobservational diagnostic model development | Other | – |
Primary outcome
Overall Diagnostic Accuracy
Time frame: From may 2025 to may 2027
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Aug 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Learning and Ultrasound, run by Peking University First Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zheng Zhang (+86 139 0137 1490, doczhz@aliyun.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall Diagnostic Accuracy (time frame: From may 2025 to may 2027). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Aug 2025.