Deep-Learning for Automatic Polyp Detection During Colonoscopy

The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.

Study Overview

• Status: Completed
• Conditions: Screening Colonoscopy
• Intervention / Treatment: Device: Computer Algorithm

• Study Type: Interventional
• Enrollment (Actual): 5
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • New York, New York, United States, 10016
      • NYU Langone Health

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 99 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Patients presenting for routine colonoscopy for screening and/or surveillance purposes.
• Ability to provide written, informed consent and understand the responsibilities of trial participation

Exclusion Criteria:

• People with diminished cognitive capacity.
• The subject is pregnant or planning a pregnancy during the study period.
• 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 with any polypoid/ulcerated lesion > 20mm concerning for invasive cancer on endoscopy.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Screening Colonoscopy; Patients undergoing standard screening or surveillance colonoscopy will be included	Device: Computer Algorithm; This device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Adenoma Detection Rate	the proportion of colonoscopic examinations performed that detect one or more polyp	1 Day

Collaborators and Investigators

• Sponsor: NYU Langone Health
• Investigators: Principal Investigator: Seth Gross, MD, NYU Langone Health

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2018
• Primary Completion (Actual): July 7, 2019
• Study Completion (Actual): July 7, 2019

Study Registration Dates

• First Submitted: August 16, 2018
• First Submitted That Met QC Criteria: August 16, 2018
• First Posted (Actual): August 20, 2018

Study Record Updates

• Last Update Posted (Actual): May 15, 2020
• Last Update Submitted That Met QC Criteria: May 14, 2020
• Last Verified: May 1, 2020

More Information

Terms related to this study

• Other Study ID Numbers: 18-00746

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No