Colonic PolypRELIANTNCT04335318
Real Life AI in Polyp Detection
A study testing AI-assisted colonoscopy in people with Colonic Polyp, run by Wuerzburg University Hospital at 1 site in Germany.
Trial summary
The objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colonic Polyp
- What is being tested?
- AI-assisted colonoscopy
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 230 participants
- Where
- 1 siteGermany
- Study dates
- 1 May 2020to 1 Oct 2020
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Colonoscopies for Polyp detection
Who cannot join
- Colonoscopies for Inflammatory Bowel Disease (IBD).
- Colonoscopies for work up of an active bleeding
Trial locations and contacts
1 site in 1 country.
About this study
The objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Moreover, an extensive characterization of the performance of this device will be done.
Read the detailed description
Recently, there have been remarkable breakthroughs in the introduction of deep learning techniques, especially convolutional neural networks (CNNs), in assisting clinical diagnosis in different medical fields. One of these artificial intelligence (AI) devices to diagnose colon polyps during colonoscopy was launched in October 2019. Its intended use is to work as an adjunct to the endoscopist during a colonoscopy with the purpose of highlighting regions with visual characteristics consistent with different types of mucosal abnormalities.
It is essential to know whether deep learning algorithms can really help endoscopists during colonoscopies. Several studies have already addressed this issue with different approaches and results. However, one common drawback of these type of Machine vs Human retrospective studies is endoscopist bias. It is usually generated because of human natural competitive spirit against machine or human relaxation because of AI-reliance. This can have an effect in the overall results.
The investigators perfomed colonoscopies with the use of a commercially available AI system to detect colonic polyps and recorded them during clinical routine. Additionally from March 2019 - May 2019, 120 colonoscopy videos were performed and captured prospectively without the use of AI.
In this study, the investigators plan to retrospectively compare those two video sets regarding the polyp detection rate, withdrawal time and polyp identification characteristics of the AI system.
- Conditions
- Colonic Polyp
- Being tested
- AI-assisted colonoscopy
- Keywords
- Deep Learning, Polyp detection rate, CNN
- 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-assisted colonoscopyDevice
Colonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp.
| Study group | Role | What they receive |
|---|---|---|
| Colonoscopy with AI-assistance group | Experimental | Colonoscopies were performed with AI-assistance. Receives: Device: AI-assisted colonoscopy |
| Standard Colonoscopy group | No intervention | Standard clinical procedure |
What is being measured
The primary outcome is the main question the study is designed to answer.
Polyp detection rate comparison
Number of polyps detected divided by number of colonoscopies
Measured: 45 minutes
Mean withdrawal time comparison
Mean withdrawal time comparison
Measured: 45 minutes
Secondary outcomes (4)
AI-Polyp bounding boxes - True Positive Evaluation
2 approaches: frame by frame analysis and temporal coherence analysis
Measured: 45 minutes
AI-Polyp bounding boxes - False Positive Quantitative Evaluation
3 approaches depending on window-time detection
Measured: 45 minutes
AI-Polyp bounding boxes - False Negative Evaluation
Number of by bounding box missed polyps
Measured: 45 minutes
Reaction Time Analysis
Comparison time of polyp detection in a human vs machine approach
Measured: 45 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Apr 2020
- Study start1 May 2020
- Primary completion31 Aug 2020
- Study completion1 Oct 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- 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
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 230 participants (actual)
Sponsor and investigators
- Lead sponsor
- Wuerzburg University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Alexander Hann, Wuerzburg University Hospital
- Investigators
- Alexander Hann, PD Dr. Med, Principal investigator, Wuerzburg University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04335318
- Sponsor's protocol ID
- AI01
- Acronym
- RELIANT
- Status last verified
- Apr 2021
- First submitted
- 2 Apr 2020
- Last updated
- 8 Apr 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real Life AI in Polyp Detection
- ClinicalTrials.gov ID
- NCT04335318
- Acronym / sponsor ID
- RELIANT / AI01
- Conditions
- Colonic Polyp
- Keywords
- Deep Learning, Polyp detection rate, CNN
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 230 participants (actual)
- Lead sponsor
- Wuerzburg University HospitalUniversity, hospital or other
- Study start
- 1 May 2020 (actual)
- Primary completion
- 31 Aug 2020 (actual)
- Study completion
- 1 Oct 2020 (actual)
- First posted
- 6 Apr 2020 (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 objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Moreover, an extensive characterization of the performance of this device will be done.
Detailed description
Recently, there have been remarkable breakthroughs in the introduction of deep learning techniques, especially convolutional neural networks (CNNs), in assisting clinical diagnosis in different medical fields. One of these artificial intelligence (AI) devices to diagnose colon polyps during colonoscopy was launched in October 2019. Its intended use is to work as an adjunct to the endoscopist during a colonoscopy with the purpose of highlighting regions with visual characteristics consistent with different types of mucosal abnormalities.
It is essential to know whether deep learning algorithms can really help endoscopists during colonoscopies. Several studies have already addressed this issue with different approaches and results. However, one common drawback of these type of Machine vs Human retrospective studies is endoscopist bias. It is usually generated because of human natural competitive spirit against machine or human relaxation because of AI-reliance. This can have an effect in the overall results.
The investigators perfomed colonoscopies with the use of a commercially available AI system to detect colonic polyps and recorded them during clinical routine. Additionally from March 2019 - May 2019, 120 colonoscopy videos were performed and captured prospectively without the use of AI.
In this study, the investigators plan to retrospectively compare those two video sets regarding the polyp detection rate, withdrawal time and polyp identification characteristics of the AI system.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Colonoscopy with AI-assistance group | Experimental | Device: AI-assisted colonoscopyColonoscopies were performed with AI-assistance. |
| Standard Colonoscopy group | No intervention | –Standard clinical procedure |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Colonoscopies for Polyp detection
Key exclusion criteria (2 listed)
- Colonoscopies for Inflammatory Bowel Disease (IBD).
- Colonoscopies for work up of an active bleeding
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-assisted colonoscopyColonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp. | Device | Colonoscopy with AI-assistance group |
Primary outcome
Polyp detection rate comparison
Time frame: 45 minutes
Number of polyps detected divided by number of colonoscopies
Mean withdrawal time comparison
Time frame: 45 minutes
Mean withdrawal time comparison
4 secondary outcomes
AI-Polyp bounding boxes - True Positive Evaluation
Time frame: 45 minutes
AI-Polyp bounding boxes - False Positive Quantitative Evaluation
Time frame: 45 minutes
AI-Polyp bounding boxes - False Negative Evaluation
Time frame: 45 minutes
Reaction Time Analysis
Time frame: 45 minutes
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 8 Apr 2021.
- 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.
Alexander Hann, PD Dr. Med principal investigator
Wuerzburg University 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 AI-assisted colonoscopy in people with Colonic Polyp, run by Wuerzburg University Hospital at 1 site in Germany.
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 1 inclusion and 2 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 8 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 Germany. 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?
Polyp detection rate comparison (time frame: 45 minutes). 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?
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 8 Apr 2021.