BLAInostic: Bladder Cancer Detection Using Convolutional Neural Networks
Study Details
Study Description
Brief Summary
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Detailed Description
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Detecting bladder tumor Patients with hematuria, or previous bladder tumor |
Diagnostic Test: Al_bladder
Detection of bladder tumor with help of Artificial intelligence
|
Outcome Measures
Primary Outcome Measures
- Comparing standard technique to Machine Learning [5 years]
The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy
Secondary Outcome Measures
- Detecting accuracy of subtypes of bladder cancer [5 years]
The abelity of Machine Learning to identify high grad bladder cancer from low grad bladder cancer
Eligibility Criteria
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
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Zealand University Hospital | Roskilde | Denmark | 4000 |
Sponsors and Collaborators
- Zealand University Hospital
Investigators
- Principal Investigator: Nessn Azawi, phd, Zealand University Hospital
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- SJ-905