Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (AI) System

NCT04693078 | Completed Results DMC

• 1 other identifier: 0309-19-SZMC
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

Colonoscopy is the gold standard for detection and removal of precancerous lesions, and has been amply shown to reduce mortality. However, the miss rate for polyps during colonoscopies is 22-28%, while 20-24% of the missed lesions are histologically confirmed precancerous adenomas. To address this shortcoming, the investigators propose a new polyp detection system based on deep learning, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. The investigators dub the system DEEP: (DEEP) DEtection of Elusive Polyps. The DEEP system was trained on 3,611 hours of colonoscopy videos derived from two sources, and was validated on a set comprising 1,393 hours of video, coming from a third, unrelated source. For the validation set, the ground truth labelling was provided by offline gastroenterologist annotators, who were able to watch the video in slow-motion and pause/rewind as required; two or three specialist annotators examined each video. This is a prospective, non-blinded, non-randomized pilot study of patients undergoing elective screening and surveillance colonoscopies using DEEP. The aim of the study is to: Assess the:

1. 1.Number of additional polyps detected by the DEEP system in real time colonoscopy.
2. 2.Safety by prospective assessment of the rate of adverse events during the study period attributed or not to the use of the DEEP system.
3. 3.Stability of the DEEP system by measuring the rate of false positives (False Alarms) per colonoscopies 4 And to examine its feasibility and usefulness of in clinical practice by assessing the colonoscopist user experience while using the DEEP system in a 5 point scale.

Conditions

Colonic Polyp

Keywords

Colonoscopy Performance

Outcome Measures

Primary Outcomes (2)

• Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy

  During the colonoscopy procedure, in real time when a polyp is found, the colonoscopist will rate the polyp as an elusive polyp detected by the system that might have been missed or a polyp that would have been detected with or without the system. The outcome measure will be reported as the average of additional polyps detected per colonoscopy by the DEEP system

  Through study completion, an average of 12 months

• The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System

  Prospective assessment adverse events during the study. The following adverse event will be monitored: Perforation, bleeding, and cardiorespiratory adverse events during the procedure

  Until discharge, assessed up to 7 days

Secondary Outcomes (2)

• Rate of False Positives (False Alarms) Per Colonoscopy

  Through study completion, an average of 12 months

• Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale

  Through study completion, an average of 12 months

Study Arms (1)

Intervention Arm

EXPERIMENTAL

Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure.

Device: AI polyp detection system based on deep learning

Interventions

AI polyp detection system based on deep learning DEVICE

A Polyp detection system based on deep learning and artificial intelligence, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy.

Intervention Arm

Eligibility Criteria

• Age: 40 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Healthy subjects undergoing routine screening or surveillance colonoscopy in an ambulatory non urgent setting.
• Able to understand the study protocol and sign inform consent.

You may not qualify if:

• Previous surgery involving the colon or rectum
• Known diagnosis of colorectal cancer
• Known history of inflammatory bowel disease
• Known or suspected diagnosis of familial polyposis syndrome

Sponsors & Collaborators

• Shaare Zedek Medical Center: lead
• Google LLC.: collaborator

Study Sites (1)

Digestive Diseases Institute, Shaare Zedek Medical Center

Jerusalem, 90301, Israel

Location

Related Publications (1)

• Livovsky DM, Veikherman D, Golany T, Aides A, Dashinsky V, Rabani N, Ben Shimol D, Blau Y, Katzir L, Shimshoni I, Liu Y, Segol O, Goldin E, Corrado G, Lachter J, Matias Y, Rivlin E, Freedman D. Detection of elusive polyps using a large-scale artificial intelligence system (with videos). Gastrointest Endosc. 2021 Dec;94(6):1099-1109.e10. doi: 10.1016/j.gie.2021.06.021. Epub 2021 Jun 30.

  PMID: 34216598 DERIVED

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

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

Results Point of Contact

• Title: Dr. Dan Meir Livovsky
• Organization: Shaare Zedek Medical Center

danlivo@yahoo.com

+34603833576

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

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

Study Record Dates

• First Submitted: July 1, 2020
• First Posted: January 5, 2021
• Study Start: May 18, 2020
• Primary Completion: November 30, 2020
• Study Completion: December 30, 2020
• Last Updated: March 3, 2021
• Results First Posted: March 3, 2021

Record last verified: 2021-02

Data Sharing

• IPD Sharing: Will not share

Locations

IL (1)