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Completed

NCT Number: NCT04693078

Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (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.

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Key information

Conditions

Colonic Polyp Colonic Polyps Intestinal Polyps Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Polyps

Age range

40 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Digestive Diseases Institute, Shaare Zedek Medical Center

Jerusalem, 90301, Israel

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

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

    Time frame: Through study completion, an average of 12 months

    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

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

    Time frame: Until discharge, assessed up to 7 days

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

Secondary outcomes

  1. Rate of False Positives (False Alarms) Per Colonoscopy

    Time frame: Through study completion, an average of 12 months

    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

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

    Time frame: Through study completion, an average of 12 months

    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.

Sponsors and collaborators

Lead sponsor

Shaare Zedek Medical Center

Other

Collaborators

  • Google LLC.

Registry information

Official study title

Detection of Colonic Polyps Via a Large Scale AI System

Important dates

Study start
2020
Primary completion
2020
Study completion
2020
First posted
Jan 5, 2021
Registry last updated
Mar 3, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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