Computer Aided Detection, Tandem Colonoscopy Study

NCT ID: NCT04074577

Last Updated: 2021-06-29

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 32 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-09-17
• Study Completion Date: 2020-10-14

Brief Summary

The purpose of this project is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine in real-time how a computer-aided detection (CADe) algorithm will perform when compared to standard screening or surveillance colonoscopy alone. Design will be a multi-center, prospective, unblinded randomized tandem colonoscopy study. 196 patients referred for either screening or surveillance colonoscopy will be included.

Conditions

Colonic Diseases

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: NONE

Study Groups

Standard Technique followed by Combination

Back-to-back tandem colonoscopies by the same endoscopist using an Olympus 190 colonoscope. The first colonoscopy will be performed without automated polyp detection software (standard technique) followed immediately by another colonoscopy with automated polyp detection software (combination technique).

Group Type: ACTIVE_COMPARATOR

Standard technique First

Intervention Type: DIAGNOSTIC_TEST

colonoscopy without automated polyp detection software

Combination +followed by Standard Technique

Back-to-back tandem colonoscopies by the same endoscopist using an Olympus 190 colonoscope. In this arm, the first colonoscopy with be performed with automated polyp detection software (combination technique) followed immediately by another colonoscopy without automated polyp detection software (standard technique)

Group Type: ACTIVE_COMPARATOR

combination technique First

Intervention Type: DIAGNOSTIC_TEST

automated polyp detection software

Interventions

Standard technique First

colonoscopy without automated polyp detection software

Intervention Type: DIAGNOSTIC_TEST

combination technique First

automated polyp detection software

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Patients presenting to the endoscopy unit for a screening colonoscopy or a surveillance colonoscopy
• Willingness to undergo tandem colonscopies with and without the use of computer-aided software while undergoing conventional colonoscopy with sedation
• Ability to provide written, informed consent (approved by IRB) and understand the responsibilities of trial participation

Exclusion Criteria

• People with diminished cognitive capacity
• Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
• Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
• Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
• Patients with inflammatory bowel disease
• Patients referred for endoscopic mucosal resection (EMR)

• Minimum Eligible Age: 22 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

NYU Langone Health

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Seth Gross, MD

Role: PRINCIPAL_INVESTIGATOR

New York Langone Health

Locations

NYU Langone Health

New York, New York, United States

Countries

United States

Other Identifiers

19-00801

Identifier Type: -

Identifier Source: org_study_id