• A Study of Blue Light Flexible Cystoscopy With Cysview in the Detection of Bladder Cancer in the Surveillance Setting

  NCT02560584

  CompletedInterventionalPhase 3Updated Jul 2, 2018
• Cystoscopic Imaging Collection and Enhancement pROject

  NCT02427321

  CompletedObservationalUpdated Jan 14, 2016
• Outcomes of High-risk Non-muscle Invasive Bladder Cancer Treated With Blue Light Resection

  NCT06525571

  RecruitingInterventionalPhase 3Updated Nov 18, 2025
• Study of bladdeR Cancer Detection in Standard White Light Versus AI-Supported Endoscopy-02

  NCT06780358

  Enrolling by invitationInterventionalNot ApplicableUpdated Jan 17, 2025
• Blue Light Cystoscopy With Cysview® Registry

  NCT02660645

  RecruitingObservationalUpdated Dec 18, 2025
• Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence

  NCT05415631

  RecruitingObservationalUpdated Jul 15, 2022

ClinicalTrials.gov ID

NCT07144319

Lead Sponsor

PhotocureINDUSTRY

Overall Status

Recruiting

Study Type

Observational

Enrollment

500Estimated

Last Update Posted

Aug 14, 2026Actual

Start Date

Mar 25, 2026Actual

Primary Completion Date

Dec 2027Estimated

Completion Date

Dec 2027Estimated

Official Title

Exploration of Novel AI-enabled Blue Light Enhanced Cystoscopy

Brief Summary

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

• Bladder Cancer
• Non-muscle Invasive Bladder Cancer

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

No

Eligibility Criteria

Inclusion Criteria:

• Age 18 or older
• Written informed consent, approved by relevant IRB/IEC, signed
• Hexvix/Cysview has been prescribed in the usual manner in accordance with the terms of the marketing authorization (see Appendix B)
• Physician has planned to do a blue light cystoscopy on the patient and to obtain biopsies, if clinically indicated, of suspicious lesions with video confirmation.
• Patient has not previously taken part in this study

Exclusion Criteria:

• None

Observational Model

Cohort

Time Perspective

Prospective

Lead Sponsor

PhotocureINDUSTRY

urologie@uzleuven.be+3216346930