Exploration of Novel AI-enabled Blue Light Enhanced Cystoscopy
Public ClinicalTrials.gov record NCT07144319. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
Blue light cystoscopy (BLC) is a diagnostic procedure in bladder cancer where the inside of the bladder is observed with a camera to detect bladder lesions. Unlike regular white light cystoscopy, blue light cystoscopy makes use of a drug that induces fluorescence under blue light preferentially in neoplastic and malignant cells that helps visualize bladder lesions during the cystoscopic procedure. Blue light cystoscopy has shown to improve detection of bladder cancer. Cystoscopy, including blue light cystoscopy, is a procedure involving assessment of the visual appearance of the bladder surface, leading to decisions of taking biopsies, remove suspicious areas and assign treatment options. The assessment is subjective and has a large operator variability. These shortcomings show an opportunity for computer aided detection (CADe) medical device to add value to both clinicians and patients. The objective of this data collection study is to build a high-quality, diverse data set of video, image recordings and relevant clinical data from BLC procedures performed as part of routine clinical practice to train a computer-aided detection (CADe) algorithm for real- time lesion detection during cystoscopy. The data will be used to support the training, non-clinical technical development and testing of such AI algorithms for use during cystoscopy and to provide documentation needed for training of such algorithms and to assist in guiding future validation of such algorithms. Exploratory purposes of the study is to use data to explore future AI algorithms in bladder cancer, such as computer-aided diagnosis (CADx) AI algorithms, image enhancement and cystoscopy improvement algorithms, including bladder mapping, tumor visualization, cystoscopy documentation, and combination models of image and clinical data including risk assessment, clinical outcomes, and disease modeling
Study identification
- NCT ID
- NCT07144319
- Recruitment status
- Recruiting
- Study type
- Observational
- Phase
- Not listed
- Enrollment
- 500 participants
Conditions and interventions
Conditions
Interventions
Not listed
Eligibility (public fields only)
- Age range
- 18 Years and older
- Sex
- All
- Healthy volunteers
- Healthy volunteers not accepted
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Mar 24, 2026
- Primary completion
- Nov 30, 2027
- Completion
- Nov 30, 2027
- Last update posted
- Sep 3, 2026
2026 – 2027
United States locations
- U.S. sites
- 3
- U.S. states
- 3
- U.S. cities
- 3
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| Moffitt Cancer Center | Tampa | Florida | 33612 | Recruiting |
| Regents of the University of Michigan | Ann Arbor | Michigan | 48108 | Recruiting |
| Rutgers Cancer Institute | New Brunswick | New Jersey | 08901 | Recruiting |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
Non-U.S. locations
This page focuses on the U.S. directory. The official record also lists 5 non-U.S. sites.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT07144319, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted Sep 3, 2026 · Synced Sep 5, 2026
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Open the official record
The complete protocol, eligibility criteria, and contact information for NCT07144319 live on ClinicalTrials.gov.