Trial Outcomes & Findings for Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (AI) System (NCT NCT04693078)

Results Overview

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

Recruitment status

COMPLETED

Study phase

NA

Target enrollment

100 participants

Primary outcome timeframe

Through study completion, an average of 12 months

Results posted on

2021-03-03

Participant Flow

Participant milestones

Participant milestones
Measure	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. 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.
Overall Study STARTED	100
Overall Study COMPLETED	100
Overall Study NOT COMPLETED	0

Reasons for withdrawal

Withdrawal data not reported

Baseline Characteristics

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

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	Intervention Arm n=100 Participants Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. 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.
Age, Continuous	60.4 years STANDARD_DEVIATION 10.7 • n=5 Participants
Sex: Female, Male Female	44 Participants n=5 Participants
Sex: Female, Male Male	56 Participants n=5 Participants
Ethnicity (NIH/OMB) Hispanic or Latino	0 Participants n=5 Participants
Ethnicity (NIH/OMB) Not Hispanic or Latino	100 Participants n=5 Participants
Ethnicity (NIH/OMB) Unknown or Not Reported	0 Participants n=5 Participants
Region of Enrollment Israel	100 participants n=5 Participants

PRIMARY outcome

Timeframe: 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

Outcome measures

Outcome measures
Measure	Intervention Arm n=100 Participants Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. 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.
Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy	0.89 Added polyps detected per colonoscopy Interval 0.66 to 1.12

PRIMARY outcome

Timeframe: 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

Outcome measures

Outcome measures
Measure	Intervention Arm n=100 Participants Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. 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.
The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System	0 Participants

SECONDARY outcome

Timeframe: 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

Outcome measures

Outcome measures
Measure	Intervention Arm n=100 Participants Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. 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.
Rate of False Positives (False Alarms) Per Colonoscopy	3.87 False positive alarms per colonoscopy Interval 3.34 to 4.4

SECONDARY outcome

Timeframe: 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.

Outcome measures

Outcome measures
Measure	Intervention Arm n=100 Participants Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. 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.
Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale	3.9 score on a scale Interval 3.7 to 4.1

Adverse Events

Intervention Arm

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Serious adverse events

Adverse event data not reported

Other adverse events

Adverse event data not reported

Additional Information

Dr. Dan Meir Livovsky

Shaare Zedek Medical Center

Phone: +34603833576

Email: [email protected]

Results disclosure agreements

Principal investigator is a sponsor employee
Publication restrictions are in place