Clinical vAliDation of ARTificial Intelligence in POlyp Detection

NCT ID: NCT04442607

Last Updated: 2022-11-30

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 856 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-10-13
• Study Completion Date: 2022-11-29

Brief Summary

This study is an open label, unblinded, non-randomized interventional study, comparing the investigational artificial intelligence tool with the current "gold standard": Data acquisition will be obtained during one scheduled colonoscopic procedure by a trained endoscopist. During insertion, no action will be taken, colonoscopy is performed following the standard of care. Once withdrawal is started, a second observer (not a trained endoscopist but person trained in polyp recognition) will start the bedside Artificial intelligence (AI) tool, connected to the endoscope's tower, for detection. This second observer is trained in assessing endoscopic images to define the AI tool's outcome. Due to the second observer watching the separate AI screen, the endoscopist is blinded of the AI outcome. When a detection is made by the AI system that is not recognized by the endoscopist, the endoscopist will be asked to relocate that same detection and to reassess the lesion and the possible need of therapeutic action. All detections are separately counted and categorized by the second observer. All polyp detections will be removed following standard of care for histological assessment. The entire colonoscopic procedure is recorded via a separate linked video-recorder.

Detailed Description

This is an investigator-initiated non-randomized prospective interventional trial to validate the performance of a novel state-of-the-art computer-aided detection (CADe) tool for colorectal polyp detection implemented as second observer during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred for a screening, surveillance or diagnostic colonoscopy will be included.

Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to count the number of detections made by the AI system and categorize the results into positive or negative results as follows (1) true positive, (2) false negative or (3) false positive. In case of a detection of the AI-system that was not seen by the endoscopist or unclear to the second observer, the second observer will ask to re-evaluate the indicated region to determine whether after second look the endoscopist has to take extra action. The entire procedure will be recorded.

There are no additional risks specific to the use of the AI tool to be taken into account. General risk of colonoscopy (i.e.: perforation, bleeding or post-polypectomy syndrome) could occur with the same frequency as that of a colonoscopy without the use of this AI tool.

All patients will receive a standard of care protocol during their colonoscopy. The AI system can only have a beneficial outcome for the patient, a better polyp detection, as it has shown to be non-inferior in terms of accuracy when compared to high detecting endoscopist in our pilot trial

Conditions

Polyp of Colon

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

AI arm

Only one arm in this study. Every patient who is eligible for this study and is included, after informed consent, will receive a standard colonoscopy combined with real-time AI video analysis

Group Type: EXPERIMENTAL

artificial intelligence image processing

Intervention Type: DEVICE

Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to count the number of detections made by the AI system and categorize the results into positive or negative results as follows (1) true positive, (2) false negative or (3) false positive.

Interventions

artificial intelligence image processing

Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to count the number of detections made by the AI system and categorize the results into positive or negative results as follows (1) true positive, (2) false negative or (3) false positive.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Age ≥40 years
• Referral for screening, surveillance or diagnostic colonoscopy
• Able to give informed consent by the patient or by a legal representative

Exclusion Criteria

• <40 years old
• Referral for a therapeutic colonoscopy
• Known Lynch syndrome or Familial Adenomatous Polyposis syndrome
• Any contraindication for colonoscopy or biopsies of the colon
• Uncontrolled coagulopathy
• Confirmed diagnosis of inflammatory bowel disease prior to the scheduled colonoscopy
• Short bowel or ileostomy
• Pregnancy

• Colonic inflammation of > 30cm during colonoscopy
• Incomplete colonoscopy for any reason
• Incomplete recording or technical failure of the artificial intelligence system

• Minimum Eligible Age: 40 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Nuovo Regina Margherita Hospital, Rome, Italy

UNKNOWN

Sponsor Role: collaborator

Krankenhaus Barmherzige Brüder, Regensburg, Germany

UNKNOWN

Sponsor Role: collaborator

Centre Hospitalier Universitaire de Nantes, Nantes, France

UNKNOWN

Sponsor Role: collaborator

Centrum Onkologii-Instytut im. Marii Skłodowskiej-Curie, Warschau, Poland

UNKNOWN

Sponsor Role: collaborator

Spire Portsmouth Hospital, Portsmouth, United Kingdom

UNKNOWN

Sponsor Role: collaborator

University Medical Center, Amsterdam, The Netherlands

UNKNOWN

Sponsor Role: collaborator

University Hospitals Ghent, Ghent, Belgium

UNKNOWN

Sponsor Role: collaborator

Universitaire Ziekenhuizen KU Leuven

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Raf Bisschops, MD,PhD

Role: PRINCIPAL_INVESTIGATOR

Universitaire Ziekenhuizen KU Leuven

Locations

University Hospitals Leuven

Leuven, Vlaams-Brabant, Belgium

Countries

Belgium

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Document Type: Informed Consent Form

View Document

Other Identifiers

S64243

Identifier Type: -

Identifier Source: org_study_id