Screening ColonoscopyNCT03637712
Deep-Learning for Automatic Polyp Detection During Colonoscopy
A study testing Computer Algorithm in people with Screening Colonoscopy, run by NYU Langone Health at 1 site in United States.
Trial summary
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard… Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Screening Colonoscopy
- What is being tested?
- Computer Algorithm
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 5 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Sep 2018to 7 Jul 2019
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients presenting for routine colonoscopy for screening and/or surveillance purposes.
- Ability to provide written, informed consent and understand the responsibilities of trial participation
Who cannot join
- People with diminished cognitive capacity.
- The subject is pregnant or planning a pregnancy during the study period.
- Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
- Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
- Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
- Patients with inflammatory bowel disease
- Patients with any polypoid/ulcerated lesion > 20mm concerning for invasive cancer on endoscopy.
Trial locations and contacts
1 site in 1 country.
About this study
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
- Conditions
- Screening Colonoscopy
- Being tested
- Computer Algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Computer AlgorithmDevice
This device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps
| Study group | Role | What they receive |
|---|---|---|
| Screening Colonoscopy | Experimental | Patients undergoing standard screening or surveillance colonoscopy will be included Receives: Device: Computer Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Adenoma Detection Rate
the proportion of colonoscopic examinations performed that detect one or more polyp
Measured: 1 Day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Aug 2018
- Study start1 Sep 2018
- Primary completion7 Jul 2019
- Study completion7 Jul 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Screening ColonoscopyStudies of the same condition, open and under way.
- More trials run by NYU Langone HealthThe sponsor's other registered studies.
- 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
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 5 participants (actual)
Sponsor and investigators
- Lead sponsor
- NYU Langone Health
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Seth Gross, MD, Principal investigator, NYU Langone Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03637712
- Sponsor's protocol ID
- 18-00746
- Status last verified
- May 2020
- First submitted
- 16 Aug 2018
- Last updated
- 15 May 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep-Learning for Automatic Polyp Detection During Colonoscopy
- ClinicalTrials.gov ID
- NCT03637712
- Acronym / sponsor ID
- 18-00746
- Conditions
- Screening Colonoscopy
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 5 participants (actual)
- Lead sponsor
- NYU Langone HealthUniversity, hospital or other
- Study start
- 1 Sep 2018 (actual)
- Primary completion
- 7 Jul 2019 (actual)
- Study completion
- 7 Jul 2019 (actual)
- First posted
- 20 Aug 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Screening Colonoscopy | Experimental | Device: Computer AlgorithmPatients undergoing standard screening or surveillance colonoscopy will be included |
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Patients presenting for routine colonoscopy for screening and/or surveillance purposes.
- Ability to provide written, informed consent and understand the responsibilities of trial participation
Key exclusion criteria (7 listed)
- People with diminished cognitive capacity.
- The subject is pregnant or planning a pregnancy during the study period.
- Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
- Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
- Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
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 |
|---|---|---|
| Computer AlgorithmThis device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps | Device | Screening Colonoscopy |
Primary outcome
Adenoma Detection Rate
Time frame: 1 Day
the proportion of colonoscopic examinations performed that detect one or more polyp
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2020.
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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.
Seth Gross, MD principal investigator
NYU Langone Health
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Computer Algorithm in people with Screening Colonoscopy, run by NYU Langone Health at 1 site in United States.
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 7 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 15 May 2020. 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 United States. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Adenoma Detection Rate (time frame: 1 Day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 15 May 2020.