Colonic PolypNCT04693078
Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (AI) System
A study testing AI polyp detection system based on deep learning in people with Colonic Polyp, run by Shaare Zedek Medical Center at 1 site in Israel.
Trial 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. Read more about this study
- Who is it for?
- Ages 40 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Colonic Polyp
- What is being tested?
- AI polyp detection system based on deep learning
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteIsrael
- Study dates
- 18 May 2020to 30 Dec 2020
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Healthy subjects undergoing routine screening or surveillance colonoscopy in an ambulatory non urgent setting.
- Able to understand the study protocol and sign inform consent.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Israel1 site
Digestive Diseases Institute, Shaare Zedek Medical Center
Jerusalem, 90301
About this study
Official title: 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.
Read more
The aim of the study is to:
Assess the:
- Number of additional polyps detected by the DEEP system in real time colonoscopy.
- Safety by prospective assessment of the rate of adverse events during the study period attributed or not to the use of the DEEP system.
- 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
- Being tested
- AI polyp detection system based on deep learning
- Keywords
- Colonoscopy Performance
- Medical subject headings
- Colonic Polyps
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI polyp detection system based on deep learningDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: AI polyp detection system based on deep learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: Until discharge, assessed up to 7 days
Secondary outcomes (2)
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
Measured: 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.
Measured: Through study completion, an average of 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jan 2021
- Study start18 May 2020
- Primary completion30 Nov 2020
- Study completion30 Dec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Colonic PolypStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shaare Zedek Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Google LLC.
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04693078
- Sponsor's protocol ID
- 0309-19-SZMC
- Status last verified
- Feb 2021
- First submitted
- 1 Jul 2020
- Last updated
- 3 Mar 2021
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data will be shared only on request and after consent form the patient and the institutional ethics committee
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.
PubMed 34216598
Study at a glance
From the registry record, last updated by the sponsor on 3 Mar 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Colonic Polyps Via a Large Scale AI System
- ClinicalTrials.gov ID
- NCT04693078
- Acronym / sponsor ID
- 0309-19-SZMC
- Conditions
- Colonic Polyp
- Keywords
- Colonoscopy Performance
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Shaare Zedek Medical CenterUniversity, hospital or other
- Collaborators
- Google LLC.
- Study start
- 18 May 2020 (actual)
- Primary completion
- 30 Nov 2020 (actual)
- Study completion
- 30 Dec 2020 (actual)
- First posted
- 5 Jan 2021 (actual)
- Results
- Posted 3 Mar 2021
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Number of additional polyps detected by the DEEP system in real time colonoscopy.
- Safety by prospective assessment of the rate of adverse events during the study period attributed or not to the use of the DEEP system.
- 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 design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Intervention Arm | Experimental | Device: AI polyp detection system based on deep learningConsecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. |
Who may qualify
- Ages
- 40 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Healthy subjects undergoing routine screening or surveillance colonoscopy in an ambulatory non urgent setting.
- Able to understand the study protocol and sign inform consent.
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI polyp detection system based on deep learningA 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. | Device | Intervention Arm |
Primary outcome
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
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
2 secondary outcomes
Rate of False Positives (False Alarms) Per Colonoscopy
Time frame: Through study completion, an average of 12 months
Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale
Time frame: Through study completion, an average of 12 months
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Mar 2021.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Derived from this study: PubMed 34216598Livovsky 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.
- Individual participant dataSharing planned: No. Data will be shared only on request and after consent form the patient and the institutional ethics committee
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI polyp detection system based on deep learning in people with Colonic Polyp, run by Shaare Zedek Medical Center at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Mar 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 3 Mar 2021). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy (time frame: Through study completion, an average of 12 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04693078, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Mar 2021.