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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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| BLC patients | Adult, consenting patients scheduled for BLC as part of clinical practice. |
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| Measure | Description | Time Frame |
|---|---|---|
| Video and image collection | To collect videos, images and relevant clinical data from BLC procedures performed as part of clinical practice. The data will be used to explore the potential of a BLC-enabled AI algorithm for lesion detection of bladder cancer. | 1 day |
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Inclusion Criteria:
Exclusion Criteria:
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Routine clinical practice patients scheduled for BLC
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Kristine Young-Halvorsen, PhD | Contact | 004722062210 | research@photocure.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Moffitt Cancer Center | Recruiting | Tampa | Florida | 33612 | United States |
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| ID | Term |
|---|---|
| D001749 | Urinary Bladder Neoplasms |
| D000093284 | Non-Muscle Invasive Bladder Neoplasms |
| ID | Term |
|---|---|
| D014571 | Urologic Neoplasms |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| Regents of the University of Michigan | Recruiting | Ann Arbor | Michigan | 48108 | United States |
|
| Rutgers Cancer Institute | Recruiting | New Brunswick | New Jersey | 08901 | United States |
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| Oslo University Hospital | Recruiting | Oslo | Norway |
|
| D052776 |
| Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D001745 | Urinary Bladder Diseases |
| D014570 | Urologic Diseases |
| D052801 | Male Urogenital Diseases |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |