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Enrolling by Invitation

NCT Number: NCT06780358

Study of bladdeR Cancer Detection in Standard White Light Versus AI-Supported Endoscopy-02

This study is being conducted to investigate if an artificial intelligence support tool is non-inferior in detecting bladder cancer compared to the traditional method, standard white light cystoscopy (WLC). The researchers will compare how well the artificial intelligence tool and WLC perform in detecting bladder cancer through a controlled, organized testing process.

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Key information

Conditions

Bladder Cancer Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Male Urogenital Diseases Neoplasms Neoplasms by Site Urinary Bladder Diseases Urinary Bladder Neoplasms Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Department of Urology, Aarhus University Hospital

Aarhus, 8200, Denmark

About this study

This clinical investigation aims to confirm that an artificial intelligence model utilizing a Convolutional Neural Network (CNN) can achieve sensitivity in detecting bladder cancer that is non-inferior to traditional white light cystoscopy (WLC) in a randomized controlled trial. The investigational artificial intelligence device leverages the advanced capabilities of CNNs, a type of deep learning model designed to analyze visual imagery with high precision.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Men and women adults, age >18 years old

Suspicion of primary or recurrent bladder cancer

Willingness to sign the Informed Consent Form (ICF) for the CI

Ability to comprehend the oral and written Patient Information Leaflet (PIL)

Exclusion criteria

  • Not able or willing to sign the Informed Consent Form

Treatment and study plan

AI supported detection of bladder cancer

Device

AI-model-supported detection of bladder cancer during white light cystoscopy

Primary outcomes

  1. Sensitivity of standard WLC compared to WLC assisted by the AI model evaluated with a non-inferiority margin of 5%.

    Time frame: 7 month

    To determine whether the AI model is non-inferior with regards to sensitivity compared to standard WLC in a randomized controlled trial.

Sponsors and collaborators

Lead sponsor

Cystotech

Industry

Collaborators

  • Aarhus University Hospital

Registry information

Acronym: RAISE02

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Jan 17, 2025
Registry last updated
Jan 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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