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

Detection of Colonic Polyps Via a Large Scale AI System

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. Number of additional polyps detected by the DEEP system in real time colonoscopy.
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. 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.

Study Overview

• Status: Completed
• Conditions: Colonic Polyp
• Intervention / Treatment: Device: AI polyp detection system based on deep learning

• Study Type: Interventional
• Enrollment (Actual): 100
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Israel
  • Jerusalem, Israel, 90301
    • Digestive Diseases Institute, Shaare Zedek Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 40 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

Inclusion Criteria:

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

Exclusion Criteria:

• 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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• 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: Intervention Arm; 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Rate of False Positives (False Alarms) Per Colonoscopy	During the colonoscopy procedure, in real time after each polyp found by the DEEP system, the colonoscopist will rate the polyp as either a true polyp or a false positive detection or a "false alarm" this measure will be reported as the average of false positive detection per colonoscopy	Through study completion, an average of 12 months
Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale	At the end of the procedures the colonoscopist will be requires to answer the question "from a scale of 1-5 how useful did you find the system in this procedure?", where higher scores represent more usefulness. This measure will be reported as the average score form all 100 procedures.	Through study completion, an average of 12 months

Collaborators and Investigators

• Sponsor: Shaare Zedek Medical Center
• Collaborators: Google LLC.

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): May 18, 2020
• Primary Completion (Actual): November 30, 2020
• Study Completion (Actual): December 30, 2020

Study Registration Dates

• First Submitted: July 1, 2020
• First Submitted That Met QC Criteria: December 31, 2020
• First Posted (Actual): January 5, 2021

Study Record Updates

• Last Update Posted (Actual): March 3, 2021
• Last Update Submitted That Met QC Criteria: February 10, 2021
• Last Verified: February 1, 2021

More Information

Terms related to this study

• Keywords: Colonoscopy Performance
• Additional Relevant MeSH Terms: Pathological Conditions, Anatomical; Intestinal Polyps; Polyps; Colonic Polyps
• Other Study ID Numbers: 0309-19-SZMC

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Data will be shared only on request and after consent form the patient and the institutional ethics committee

Drug and device information, study documents

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