Exploration of Novel AI-enabled Blue Light Enhanced Cystoscopy
This is not medical advice. AI-assisted translation — inaccuracies may occur. Always verify the original and consult your oncologist before taking any steps.
About the trial
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
Original English text from ClinicalTrials.gov
Who can (and can't) join
✓ Qualifies
- •Wiek 18 lat lub więcej
- •Podpisana zgoda na udział w badaniu
- •Pacjent ma przepisany lek Hexvix/Cysview zgodnie ze standardową praktyką
- •Lekarz planuje wykonać cystoskopię z błękitnym światłem i pobrać próbki tkanek podejrzanych zmian
- •Pacjent nie uczestniczył wcześniej w tym badaniu
Simplified criteria — AI translation
Trial details
- Minimum age
- 18 Years
- Last updated (source)
- August 14, 2026
- Sex
- No restrictions
Locations (5)
Moffitt Cancer Center
Tampa, United States
Regents of the University of Michigan
Ann Arbor, United States
Rutgers Cancer Institute
New Brunswick, United States
UZ Leuven
Leuven, Belgium
Oslo University Hospital
Oslo, Norway
Trial contact
Kristine Young-Halvorsen, PhD
Contact information from ClinicalTrials.gov. Contact in English.
Share this trial
Data from ClinicalTrials.gov. AI-assisted translation, last sync: 8/15/2026.
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