Colonic PolypsNCT03967756
Impact of Automatic Polyp Detection System on Adenoma Detection Rate
A study testing Automatic polyp detection system in people with Colonic Polyps and Colorectal Adenomas, run by Changhai Hospital at 1 site in China.
Trial summary
In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. Read more about this study
- Who is it for?
- Ages 40 to 85 years
- Conditions the study is for
- Colonic Polyps, Colorectal Adenomas
- What is being tested?
- Automatic polyp detection system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,118 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2019to 1 Oct 2021
- 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 40 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
- Patients who have signed inform consent form.
Who cannot join
- Patients who have undergone colonic resection
- Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage.
- Patients with severe chronic cardiopulmonary and renal disease.
- Patients who are unwilling or unable to consent.
- Patients who are not suitable for colonoscopy
- Patients who received urgent or therapeutic colonoscopy
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- Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction
- Patients who are taking aspirin, clopidogrel or other anticoagulants
- Patients with withdrawal time < 6 min
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Changhai Hospital, Second Military Medical University
Shanghai, 200433
zhaoshen Li, MD, 86-21-81873241, zhaoshenlismmu@gmail.com
Zhaoshen Li, MD
About this study
Official title: Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
- Conditions
- Colonic Polyps, Colorectal Adenomas
- Being tested
- Automatic polyp detection system
- Medical subject headings
- Colonic Polyps
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Automatic polyp detection systemDevice
When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.
| Study group | Role | What they receive |
|---|---|---|
| AI-assisted withdrawal group | Experimental | A deep learning-based automatic polyp detection system was used to assist the endoscopist. Receives: Device: Automatic polyp detection system |
| Routine withdrawal group | No intervention | Routine withdrawal without any assist. |
What is being measured
The primary outcome is the main question the study is designed to answer.
adenoma detection rate(ADR)
the number of patients with at least one adenoma divided by the total number of patients.
Measured: 30 minutes
Secondary outcomes (3)
polyp detection rate(PDR)
the number of patients with at least one polyp divided by the total number of patients.
Measured: 30 minutes
adenoma per colonoscopy
the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
Measured: 30 minutes
polyp per colonoscopy
the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
Measured: 30 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 May 2019
- Study start1 Jun 2019
- Primary completion20 Jul 2021 (estimated)
- Study completion1 Oct 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Colonic PolypsStudies of the same condition, open and under way.
- More trials run by Changhai HospitalThe 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
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,118 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Changhai Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The First Affiliated Hospital of Dalian Medical University, Wenzhou Central Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Responsible party
- Principal investigator, Zhaoshen Li, Changhai Hospital
- Investigators
- Zhaoshen Li, M.D, Principal investigator, Changhai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03967756
- Sponsor's protocol ID
- AI-2
- Status last verified
- Apr 2021
- First submitted
- 28 May 2019
- Last updated
- 6 Apr 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
PubMed 29928897Ahmad OF, Soares AS, Mazomenos E, Brandao P, Vega R, Seward E, Stoyanov D, Chand M, Lovat LB. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019 Jan;4(1):71-80. doi: 10.1016/S2468-1253(18)30282-6. Epub 2018 Dec 6.
PubMed 30527583
Study at a glance
From the registry record, last updated by the sponsor on 6 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT03967756
- Acronym / sponsor ID
- AI-2
- Conditions
- Colonic Polyps, Colorectal Adenomas
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 1,118 participants (estimated)
- Lead sponsor
- Changhai HospitalUniversity, hospital or other
- Collaborators
- The First Affiliated Hospital of Dalian Medical University, Wenzhou Central Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Study start
- 1 Jun 2019 (actual)
- Primary completion
- 20 Jul 2021 (estimated)
- Study completion
- 1 Oct 2021 (estimated)
- First posted
- 30 May 2019 (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.
In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-assisted withdrawal group | Experimental | Device: Automatic polyp detection systemA deep learning-based automatic polyp detection system was used to assist the endoscopist. |
| Routine withdrawal group | No intervention | –Routine withdrawal without any assist. |
Who may qualify
- Ages
- 40 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
- Patients who have signed inform consent form.
Key exclusion criteria (9 listed)
- Patients who have undergone colonic resection
- Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage.
- Patients with severe chronic cardiopulmonary and renal disease.
- Patients who are unwilling or unable to consent.
- Patients who are not suitable for colonoscopy
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 |
|---|---|---|
| Automatic polyp detection systemWhen colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm. | Device | AI-assisted withdrawal group |
Primary outcome
adenoma detection rate(ADR)
Time frame: 30 minutes
the number of patients with at least one adenoma divided by the total number of patients.
3 secondary outcomes
polyp detection rate(PDR)
Time frame: 30 minutes
adenoma per colonoscopy
Time frame: 30 minutes
polyp per colonoscopy
Time frame: 30 minutes
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Apr 2021.
- Background: PubMed 29928897Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
- Background: PubMed 30527583Ahmad OF, Soares AS, Mazomenos E, Brandao P, Vega R, Seward E, Stoyanov D, Chand M, Lovat LB. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019 Jan;4(1):71-80. doi: 10.1016/S2468-1253(18)30282-6. Epub 2018 Dec 6.
- Individual participant dataSharing planned: No
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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.
Zhaoshen Li, M.D principal investigator
Changhai Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Automatic polyp detection system in people with Colonic Polyps and Colorectal Adenomas, run by Changhai Hospital at 1 site in China.
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 9 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: status not confirmed, as of the sponsor's last update on 6 Apr 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zhaoshen Li, M.D (86-21-31161365, li.zhaoshen@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
adenoma detection rate(ADR) (time frame: 30 minutes). 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 NCT03967756, which also lists 2 linked publications.
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 6 Apr 2021.