NCT05193656

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.

Trial Health

57
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
5,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jun 2021

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

June 1, 2021

Completed
6 months until next milestone

First Submitted

Initial submission to the registry

November 17, 2021

Completed
2 months until next milestone

First Posted

Study publicly available on registry

January 18, 2022

Completed
4.4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2026

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 1, 2026

Completed
Last Updated

January 30, 2024

Status Verified

January 1, 2024

Enrollment Period

5 years

First QC Date

November 17, 2021

Last Update Submit

January 28, 2024

Conditions

Bladder Cancer

Keywords

Machine learningArtificial intelligence

Outcome Measures

Primary Outcomes (1)

  • Comparing standard technique to Machine Learning

    The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy

    5 years

Secondary Outcomes (1)

  • Detecting accuracy of subtypes of bladder cancer

    5 years

Study Arms (1)

Detecting bladder tumor

Patients with hematuria, or previous bladder tumor

Diagnostic Test: Al_bladder

Interventions

Al_bladderDIAGNOSTIC_TEST

Detection of bladder tumor with help of Artificial intelligence

Detecting bladder tumor

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Patients with micro or macroscopic hematuria

You may qualify if:

  • Patients with first time hematuria
  • Patients with the control program for previous bladder cancer

You may not qualify if:

  • Patients with control cystoscope for noncancer suspected disease

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Zealand University Hospital

Roskilde, 4000, Denmark

RECRUITING

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic NeoplasmsUrogenital NeoplasmsNeoplasms by SiteNeoplasmsFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesUrinary Bladder DiseasesUrologic DiseasesMale Urogenital Diseases

Study Officials

  • Nessn Azawi, phd

    Zealand University Hospital

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Nessn Azawi, phd

CONTACT

26393034nesa@regionsjaelland.dk

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

November 17, 2021

First Posted

January 18, 2022

Study Start

June 1, 2021

Primary Completion

June 1, 2026

Study Completion

June 1, 2026

Last Updated

January 30, 2024

Record last verified: 2024-01

Data Sharing

IPD Sharing
Will not share

Locations

DK(1)