Clinical trial · Observational
Bladder Cancer Detection Using Convolutional Neural Networks
NCT05193656CI-TRIAL-00073375BLAInosticunknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Bladder Cancer | Malignant Bladder Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Al_bladder | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Detecting bladder tumor
- description
- Patients with hematuria, or previous bladder tumor
- interventionNames
- Diagnostic Test: Al_bladder
Primary outcomes (1)
- measure
- Comparing standard technique to Machine Learning
- timeFrame
- 5 years
- description
- The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy
Secondary outcomes (1)
- measure
- Detecting accuracy of subtypes of bladder cancer
- timeFrame
- 5 years
- description
- The abelity of Machine Learning to identify high grad bladder cancer from low grad bladder cancer
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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
References
Publications (0)
Data not yet available
No reference posted for this study.