Clinical vAliDation of ARTificial Intelligence in POlyp Detection

NCT04442607 | Completed DMC

• 1 other identifier: S64243
• Study Type: interventional
• Target: 856
• Locations: 1 country
• Sites: 1

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.

Conditions

Polyp of Colon

Keywords

colonic polyps; endoscopy; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with endoscopic diagnosis as a gold standard

  1.5 year

Secondary Outcomes (5)

• Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with histological diagnosis as a gold standard.

  1.5 year

• The number of extra detected polyps by artificial intelligence with the endoscopic diagnosis as a gold standard.

  1.5 year

• The number of extra detected polyps by artificial intelligence with the histological diagnosis as a gold standard

  1.5 year

• The endoscopist's polyp miss rate defined as the additional detection of polyps during colonoscopy

  1.5 year

• The false positive rate during clean withdrawal.

  1.5 year

Other Outcomes (6)

• Correlation between the Boston Bowel Preparation Score and the number of false positive detections during colonoscopy

  1.5 year

• Correlation between the endoscopist's historical adenoma detection rate and the number of extra detections and false negative detections by the artificial intelligence system.

  1.5 year

• Correlation between the polyp size and number of false negatives and additional detections

  1.5 year

Study Arms (1)

AI arm

EXPERIMENTAL

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

Device: artificial intelligence image processing

Interventions

artificial intelligence image processing 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.

AI arm

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• \<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

Sponsors & Collaborators

• Universitaire Ziekenhuizen KU Leuven: lead
• Nuovo Regina Margherita Hospital, Rome, Italy: collaborator
• Krankenhaus Barmherzige Brüder, Regensburg, Germany: collaborator
• Centre Hospitalier Universitaire de Nantes, Nantes, France: collaborator
• Centrum Onkologii-Instytut im. Marii Skłodowskiej-Curie, Warschau, Poland: collaborator
• Spire Portsmouth Hospital, Portsmouth, United Kingdom: collaborator
• University Medical Center, Amsterdam, The Netherlands: collaborator
• University Hospital, Ghent: collaborator

Study Sites (1)

University Hospitals Leuven

Leuven, Vlaams-Brabant, 3000, Belgium

Location

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Officials

• Raf Bisschops, MD,PhD

  Universitaire Ziekenhuizen KU Leuven

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: 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.

Study Record Dates

• First Submitted: June 18, 2020
• First Posted: June 22, 2020
• Study Start: October 13, 2020
• Primary Completion: October 28, 2022
• Study Completion: November 29, 2022
• Last Updated: November 30, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

BE (1)