Study of bladdeR Cancer Detection in Standard White Light Versus AI-Supported Endoscopy-02 (RAISE02)

Cystotech

Status: Invitation-only

Conditions: Bladder Cancer

Treatments: Device: AI supported detection of bladder cancer

Study type: Interventional

Funder types: Other; Industry

Identifiers

NCT06780358

RAISE02-24-02

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 64 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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64 participants in 2 patient groups

WLC detection

No Intervention group

Description:

Detection of bladder cancer is conducted according to state-of-the-art procedures in white light modality.

AI model - WLC supported detection

Experimental group

Description:

Detection of bladder cancer in white light supported by a pre-market AI-support tool.

Treatment:

Device: AI supported detection of bladder cancer