Colonic Polyp
Conditions
Keywords
Colonoscopy Performance
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. 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.
Interventions
DEVICEAI 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.
Sponsors
Google LLC.
Shaare Zedek Medical Center
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
40 Years to 80 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy | 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 |
| The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System | 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
| Measure | Time frame | Description |
|---|---|---|
| Rate of False Positives (False Alarms) Per Colonoscopy | 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 |
| Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale | 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. |
Countries
Israel
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| 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. | 100 |
| Total | 100 |
Baseline characteristics
| Characteristic | Intervention Arm |
|---|---|
| Age, Continuous | 60.4 years STANDARD_DEVIATION 10.7 |
| Ethnicity (NIH/OMB) Hispanic or Latino | 0 Participants |
| Ethnicity (NIH/OMB) Not Hispanic or Latino | 100 Participants |
| Ethnicity (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Region of Enrollment Israel | 100 participants |
| Sex: Female, Male Female | 44 Participants |
| Sex: Female, Male Male | 56 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 100 |
| other Total, other adverse events | 0 / 100 |
| serious Total, serious adverse events | 0 / 100 |
Outcome results
Primary
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
Time frame: Through study completion, an average of 12 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention Arm | Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy | 0.89 Added polyps detected per colonoscopy |
Primary
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
Time frame: Until discharge, assessed up to 7 days
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Intervention Arm | The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System | 0 Participants |
Secondary
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.
Time frame: Through study completion, an average of 12 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention Arm | Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale | 3.9 score on a scale |
Secondary
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
Time frame: Through study completion, an average of 12 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention Arm | Rate of False Positives (False Alarms) Per Colonoscopy | 3.87 False positive alarms per colonoscopy |