Colonic PolypCADx-ProspNCT06543862
Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis
A study testing CADx (AI) system in people with Colonic Polyp and Artificial Intelligence, run by Centre hospitalier de l'Université de Montréal (CHUM) at 1 site in Canada.
Trial summary
Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. Read more about this study
- Who is it for?
- Ages 45 to 80 years
- Conditions the study is for
- Colonic Polyp, Artificial Intelligence
- What is being tested?
- CADx (AI) system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 540 participants
- Where
- 1 siteCanada
- Study dates
- 15 Nov 2024to Oct 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 45 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Indication for full colonoscopy.
Who cannot join
- Known inflammatory bowel disease
- Active colitis
- coagulopathy
- familial polyposis syndrome
- poor general health, defined as an American Society of Anesthesiologists class >3
- emergency colonoscopy
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Canada1 site, 1 recruiting
- Recruiting
Ghislaine Ahoua
Montreal, Quebec
Daniel von Renteln, MD
About this study
Official title: Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis of Colorectal Polyps
Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care.
Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.
Read the detailed description
Our study hypothesis is that for CADx implementation, instead of using the high/low confidence framework, identifying cases with suboptimal diagnostic accuracy could be facilitated through identifying cases in which CADx and endoscopist disagreed in their diagnosis. Eliminating such cases might separate out cases with low accuracy when using CADx assisted OD. Since endoscopists have a high sensitivity but low specificity for serrated polyp OD, this framework will also allow us to implement a strategy to adequately manage serrated polyps found in the cohort.
- Conditions
- Colonic Polyp, Artificial Intelligence
- Being tested
- CADx (AI) system
- Keywords
- optical diagnosis, artificial intelligence
- Medical subject headings
- Colonic Polyps
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CADx (AI) systemOther
The CADx system will be used to predict the histopathology of the polyp detected.
| Study group | Role | What they receive |
|---|---|---|
| All participants | Other | The endoscopist will make an optical diagnosis (OD) prediction for all small polyps (up to 10 mm) in white light (WL). Then, the endoscopist will make another OD prediction using image enhanced endoscopy (IEE) modes. After that, CADx will be activated in the IEE mode and a CADx prediction will be documented. Finally, after seeing the CADx prediction, the endoscopist will make a final prediction, which can agree or disagree with the autonomous CADx one. Polyps will be resected and sent to a pathology lab, where a pathologic diagnosis (blinded to the endoscopist's predictions) will be rendered. Receives: Other: CADx (AI) system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis , when histopathology results are used as the reference
Measured: up to 100 weeks
Secondary outcomes (6)
Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis
Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis, when histopathology results are used as the reference
Measured: up to 100 weeks
Test characteristics, including recall, specificity, positive and negative predictive values (PPV/NPV), and particularly the NPV of rectosigmoid neoplastic polyps.
A ≥90% NPV will be used as a quality benchmark for a strategy to not resect such diminutive polyps.
Measured: up to 100 weeks
Agreement of surveillance interval recommendations of AI-A and AI-H compared with the pathology-based recommendations
For surveillance interval assignment, the pathology results of concomitant polyps >5 mm (including multiple concomitant polyps of all sizes and histology) will be considered when calculating the surveillance interval recommendation. Surveillance recommendations will be based on the 2020 United States Multi Society Task Force Guidelines as is current standard of practice at our center.
Measured: up to 100 weeks
Proportion of patients for whom an immediate surveillance recommendation can be directly provided for each approach, and how often histopathology-based polyp examination would have been avoided.
The potential cost-effectiveness of OD (either approaches) will be evaluated using the measured described above.
Measured: up to 100 weeks
Variability of OD (AI-A and AI-H) across participating endoscopists.
Each participating endoscopist will conduct a similar number of optical diagnoses to assess endoscopist-related factors.
Measured: up to 100 weeks
Cost-effectiveness of OD ((AI-A and AI-H)
A cost-effectiveness model will be applied to better quantify costs and understand cost impact including key cost drivers when generalized to a broader screening population.
Measured: up to 100 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2024
- Study start15 Nov 2024
- Primary completionOct 2026 (estimated)
- Study completionOct 2026 (estimated)
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
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 540 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre hospitalier de l'Université de Montréal (CHUM)
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Daniel von Renteln, MD, Principal investigator, University of Montreal Medical Center (CHUM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06543862
- Sponsor's protocol ID
- 2025-12306
- Other IDs
- 2025
- Acronym
- CADx-Prosp
- Status last verified
- Sep 2026
- First submitted
- 30 Jul 2024
- Last updated
- 21 Sep 2026
- 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 21 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis of Colorectal Polyps
- ClinicalTrials.gov ID
- NCT06543862
- Acronym / sponsor ID
- CADx-Prosp / 2025-12306
- Conditions
- Colonic Polyp, Artificial Intelligence
- Keywords
- optical diagnosis, artificial intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 540 participants (estimated)
- Lead sponsor
- Centre hospitalier de l'Université de Montréal (CHUM)University, hospital or other
- Study start
- 15 Nov 2024 (actual)
- Primary completion
- Oct 2026 (estimated)
- Study completion
- Oct 2026 (estimated)
- First posted
- 9 Aug 2024 (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.
Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care.
Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.
Detailed description
Our study hypothesis is that for CADx implementation, instead of using the high/low confidence framework, identifying cases with suboptimal diagnostic accuracy could be facilitated through identifying cases in which CADx and endoscopist disagreed in their diagnosis. Eliminating such cases might separate out cases with low accuracy when using CADx assisted OD. Since endoscopists have a high sensitivity but low specificity for serrated polyp OD, this framework will also allow us to implement a strategy to adequately manage serrated polyps found in the cohort.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| All participants | Other | Other: CADx (AI) systemThe endoscopist will make an optical diagnosis (OD) prediction for all small polyps (up to 10 mm) in white light (WL). Then, the endoscopist will make another OD prediction using image enhanced endoscopy (IEE) modes. After that, CADx will be activated in the IEE mode and a CADx prediction will be documented. Finally, after seeing the CADx prediction, the endoscopist will make a final prediction, which can agree or disagree with the autonomous CADx one. Polyps will be resected and sent to a pathology lab, where a pathologic diagnosis (blinded to the endoscopist's predictions) will be rendered. |
Who may qualify
- Ages
- 45 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Indication for full colonoscopy.
Key exclusion criteria (6 listed)
- Known inflammatory bowel disease
- Active colitis
- coagulopathy
- familial polyposis syndrome
- poor general health, defined as an American Society of Anesthesiologists class >3
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 |
|---|---|---|
| CADx (AI) systemThe CADx system will be used to predict the histopathology of the polyp detected. | Other | All participants |
Primary outcome
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis
Time frame: up to 100 weeks
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis , when histopathology results are used as the reference
6 secondary outcomes
Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis
Time frame: up to 100 weeks
Test characteristics, including recall, specificity, positive and negative predictive values (PPV/NPV), and particularly the NPV of rectosigmoid neoplastic polyps.
Time frame: up to 100 weeks
Agreement of surveillance interval recommendations of AI-A and AI-H compared with the pathology-based recommendations
Time frame: up to 100 weeks
Proportion of patients for whom an immediate surveillance recommendation can be directly provided for each approach, and how often histopathology-based polyp examination would have been avoided.
Time frame: up to 100 weeks
Variability of OD (AI-A and AI-H) across participating endoscopists.
Time frame: up to 100 weeks
Cost-effectiveness of OD ((AI-A and AI-H)
Time frame: up to 100 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2026.
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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.
Daniel von Renteln, MD principal investigator
University of Montreal Medical Center (CHUM)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CADx (AI) system in people with Colonic Polyp and Artificial Intelligence, run by Centre hospitalier de l'Université de Montréal (CHUM) at 1 site in Canada.
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 6 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: looking for participants now, as of the sponsor's last update on 21 Sep 2026. 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 Canada. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Daniel von Renteln, MD (514 890-8000 ext. 30912, daniel.von.renteln.med@ssss.gouv.qc.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis (time frame: up to 100 weeks). 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 21 Sep 2026.