Detecting bladder cancer using advanced machine learning techniques
Bladder Cancer Detection Using Convolutional Neural Networks
This study is testing a new computer program that uses advanced technology to help doctors better detect and classify bladder cancer from medical images.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 5000 (estimated) |
| Sex | All |
| Sponsor | Zealand University Hospital Academic / other |
| Locations | 1 site (Roskilde) |
| Trial ID | NCT05193656 on ClinicalTrials.gov |
What this trial studies
This study focuses on utilizing deep learning architectures, specifically convolutional neural networks (CNN), to enhance the accuracy of computer-aided diagnosis (CAD) systems for bladder cancer detection. The investigators aim to analyze CT urography scans and cystoscopy images to classify bladder tumors into categories such as cancerous, non-cancerous, high grade, low grade, invasive, and non-invasive. By automating this diagnostic process, the study seeks to improve sensitivity and reduce false positive rates, ultimately aiding radiologists in creating large-scale labeled datasets more efficiently. This research represents a significant step towards developing reliable CAD systems for bladder cancer diagnosis.
Who should consider this trial
Good fit: Ideal candidates for this study include patients experiencing first-time hematuria or those under a control program for previous bladder cancer.
Not a fit: Patients undergoing cystoscopy for non-cancer suspected diseases may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to earlier and more accurate detection of bladder cancer, improving patient outcomes.
How similar studies have performed: Other studies utilizing machine learning for cancer detection have shown promising results, indicating potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients with first time hematuria * Patients with the control program for previous bladder cancer Exclusion Criteria: * Patients with control cystoscope for noncancer suspected disease
Where this trial is running
Roskilde
- Zealand University Hospital — Roskilde, Denmark (Recruiting)
Study contacts
- Principal investigator: Nessn Azawi, phd — Zealand University Hospital
- Study coordinator: Nessn Azawi, phd
- Email: nesa@regionsjaelland.dk
- Phone: 26393034
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.