Colorectal NeoplasmsCOMET-OPTICALNCT05349110
Real-time Diagnosis of Diminutive Colorectal Polyps Using AI
An observational study of Colorectal Neoplasms and Colorectal Polyp, run by Maastricht University Medical Center at 2 sites in Netherlands.
Trial summary
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colorectal Neoplasms, Colorectal Polyp
- What is being tested?
- Computer-aided diagnosis (CADx) systems
- Type of treatment
- Medical device
- How many people
- 105 participants
- Where
- 2 sitesNetherlands
- Study dates
- 20 Aug 2021to Dec 2022
- Phase
- Observational
- 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
Study population: Patient receiving a colonoscopy because of regular care will be considered eligible for inclusion if at least one diminutive colorectal polyp is encountered during the colonoscopy. Patients receive an endoscopic procedure in the context of the Dutch national screening program, because of gastrointestinal symptoms, or because of follow-up of previously diagnosed bowel diseases.
Who may be able to join
- Age >18 years;
- Patients with at least one colorectal polyps encountered during colonoscopy;
- Patients referred for a colonoscopy by the Dutch bowel cancer screening program, patients undergoing a colonoscopy for endoscopic surveillance, or patients undergoing a colonoscopy because of complaints;
- Written informed consent.
Who cannot join
- Patients with prior history of inflammatory bowel diseases (IBD) or polyposis syndromes;
- Patients with inadequate bowel preparations after adequate washing, suctioning, and cleaning manoeuvres have been performed by the endoscopist;
- Patients undergoing an emergency colonoscopy;
- Written objection in the patient file for participation in scientific research.
Trial locations and contacts
2 sites in 1 country.
Netherlands2 sites, 1 recruiting
- Completed
Catharina Ziekenhuis Eindhoven
Eindhoven, North Brabant, 5623 EJ
- Recruiting
Maastricht University Medical Center
Maastricht, Limburg, 6202AZ
Quirine van der Zander, Drs MD, 031433882241, q.vanderzander@maastrichtuniversity.nl
Erik Schoon, Prof Dr MD, 031433882241, erik.schoon@catharinaziekenhuis.nl
About this study
Official title: Real Time Computer-aided Diagnosis (CADx) of Diminutive Colorectal Polyps Using Artificial Intelligence
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Aim of this study is to investigate the real-time diagnostic performance of AI4CRP for the classification of diminutive colorectal polyps, and to compare it with the real-time diagnostic performance of commercially available CADx systems.
Read the detailed description
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Despite additional training, even experienced endoscopists continue to fail meeting international thresholds set for safe implementation of treatment strategies based on optical diagnosis.
Multiple machine learning techniques - computer-aided diagnosis (CADx) systems - have been developed for applications in medical imaging within colonoscopy and can improve endoscopic classification of colorectal polyps.
Aim of this study is to explore the feasibility of the workflow using AI4CRP (a CNN based CADx system) real-time in the endoscopy suite, and to investigate the real-time diagnostic performance of AI4CRP for the diagnosis of diminutive (<5mm) colorectal polyps. Secondary, the real-time performance of commercially available CADx systems will be investigated and compared with AI4CRP performance.
- Conditions
- Colorectal Neoplasms, Colorectal Polyp
- Being tested
- Computer-aided diagnosis (CADx) systems
- Keywords
- Artificial intelligence, Computer-aided diagnosis (CADx)
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Computer-aided diagnosis (CADx) systemsDevice
Also called AI4CRP, artificial intelligence for colorectal polyps (COMET-OPTICAL research group), CAD EYE (Fujifilm® Corporation, Tokyo, Japan)
- AI4CRP (artificial intelligence for colorectal polyps), a CNN based computer-aided diagnosis system for diagnosis of colorectal polyps (COMET-OPTICAL research group);
- CAD EYE, a computer-aided diagnosis system for diagnosis of colorectal polyps (Fujifilm® Corporation, Tokyo, Japan).
| Study group | Role | What they receive |
|---|---|---|
| Gastroenterology patients | Patient receiving a colonoscopy because of regular care will be considered eligible for inclusion if at least one diminutive colorectal polyp is encountered during the colonoscopy. Patients receive an endoscopic procedure in the context of the Dutch national screening program, because of gastrointestinal symptoms, or because of follow-up of previously diagnosed bowel diseases.
Colonoscopies will be executed using Fujifilm endoscopy systems (Fujifilm® Corporation, Tokyo, Japan), using Pentax endoscopy systems (Pentax Medical®, Hamburg, Germany), and using Olympus endoscopy systems (Olympus®, Tokyo, Japan). Receives: Device: Computer-aided diagnosis (CADx) systems |
What is being measured
The primary outcome is the main question the study is designed to answer.
Technical feasibility of real-time use of AI4CRP.
The technical feasibility of real-time use of AI4CRP in the endoscopy suite regarding a proper reception of the video output from the local endoscopy processor towards AI4CRP (in high definition quality, without any delays in time).
Measured: 6 months
User interface feasibility of real-time use of AI4CRP.
The user interface feasibility of real-time use of AI4CRP in the endoscopy suite regarding a correct alignment of the user interface of AI4CRP with the video output from the local endoscopy system (resizing image pixels and anonymization).
Measured: 6 months
The diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan). Diagnostic accuracy defined as the percentage of correctly optically diagnosed colorectal polyps.
Measured: 1 year
The sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Measured: 1 year
The specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Measured: 1 year
The negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Measured: 1 year
The positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Measured: 1 year
The Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Measured: 1 year
Secondary outcomes (21)
The diagnostic accuracy of AI4CRP per polyp.
The real-time diagnostic accuracy of AI4CRP per polyp (comprising the combination of different imaging modalities).
Measured: 1 year
The sensitivity of AI4CRP per polyp.
The real-time sensitivity of AI4CRP per polyp (comprising the combination of different imaging modalities).
Measured: 1 year
The specificity of AI4CRP per polyp.
The real-time specificity of AI4CRP per polyp (comprising the combination of different imaging modalities).
Measured: 1 year
The negative predictive value of AI4CRP per polyp.
The real-time negative predictive value of AI4CRP per polyp (comprising the combination of different imaging modalities).
Measured: 1 year
The positive predictive value of AI4CRP per polyp.
The real-time positive predictive value of AI4CRP per polyp (comprising the combination of different imaging modalities).
Measured: 1 year
The Area Under ROC Curve (AUC) of AI4CRP per polyp.
The real-time Area Under ROC Curve (AUC) of AI4CRP per polyp (comprising the combination of different imaging modalities).
Measured: 1 year
The diagnostic accuracy of CAD EYE in BLI mode, per polyp.
The real-time diagnostic accuracy of CAD EYE in BLI mode, per polyp.
Measured: 1 year
The sensitivity of CAD EYE in BLI mode, per polyp.
The real-time sensitivity of CAD EYE in BLI mode, per polyp.
Measured: 1 year
The specificity of CAD EYE in BLI mode, per polyp.
The real-time specificity of CAD EYE in BLI mode, per polyp.
Measured: 1 year
The negative predictive value of CAD EYE in BLI mode, per polyp.
The real-time negative predictive value of CAD EYE in BLI mode, per polyp.
Measured: 1 year
The positive predictive value of CAD EYE in BLI mode, per polyp.
The real-time positive predictive value of CAD EYE in BLI mode, per polyp.
Measured: 1 year
The Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
The real-time Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
Measured: 1 year
The diagnostic accuracy of AI4CRP per patient.
The real-time diagnostic accuracy of AI4CRP per patient (in case of multiple polyps per patient).
Measured: 1 year
The diagnostic accuracy of CAD EYE per patient.
The real-time diagnostic accuracy of CAD EYE per patient (in case of multiple polyps per patient).
Measured: 1 year
The localization score of AI4CRP.
The localization score of AI4CRP regarding the number of images in which the heatmap produced by AI4CRP pointed out the area of interest (scale: correct, incorrect, or partly correct area of interest).
Measured: 1 year
The difference in diagnostic accuracy of endoscopists per polyp before and after AI.
The difference in real-time diagnostic accuracy of endoscopists per polyp before and after AI.
Measured: 1 year
The difference in sensitivity of endoscopists per polyp before and after AI.
The difference in real-time sensitivity of endoscopists per polyp before and after AI.
Measured: 1 year
The difference in specificity of endoscopists per polyp before and after AI.
The difference in real-time specificity of endoscopists per polyp before and after AI.
Measured: 1 year
The difference in negative predictive value of endoscopists per polyp before and after AI.
The difference in real-time negative predictive value of endoscopists per polyp before and after AI.
Measured: 1 year
The difference in positive predictive value of endoscopists per polyp before and after AI.
The difference in real-time positive predictive value of endoscopists per polyp before and after AI.
Measured: 1 year
The agreement in surveillance interval based on optical diagnosis and histopathology.
The agreement in surveillance interval based on optical diagnosis of diminutive colorectal polyps and histopathology of small and large colorectal polyps, compared to the surveillance interval based on histopathology of all colorectal polyps (diminutive, small, and large).
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Apr 2022
- Study start20 Aug 2021
- Primary completionSep 2022 (estimated)
- Study completionDec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal NeoplasmsStudies of the same condition, open and under way.
- More trials run by Maastricht University Medical CenterThe 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 105 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Maastricht University Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Catharina Ziekenhuis Eindhoven, Eindhoven University of Technology
- Responsible party
- Sponsor
- Investigators
- Erik Schoon, Prof Dr MD, Principal investigator, Maastricht Universitair Medisch Centrum
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05349110
- Sponsor's protocol ID
- METC2021-3036
- Acronym
- COMET-OPTICAL
- Status last verified
- Apr 2022
- First submitted
- 31 Mar 2022
- Last updated
- 5 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: A data sharing plan is not yet decided on.
Study at a glance
From the registry record, last updated by the sponsor on 5 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real Time Computer-aided Diagnosis (CADx) of Diminutive Colorectal Polyps Using Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05349110
- Acronym / sponsor ID
- COMET-OPTICAL / METC2021-3036
- Conditions
- Colorectal Neoplasms, Colorectal Polyp
- Keywords
- Artificial intelligence, Computer-aided diagnosis (CADx)
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 105 participants (estimated)
- Lead sponsor
- Maastricht University Medical CenterUniversity, hospital or other
- Collaborators
- Catharina Ziekenhuis Eindhoven, Eindhoven University of Technology
- Study start
- 20 Aug 2021 (actual)
- Primary completion
- Sep 2022 (estimated)
- Study completion
- Dec 2022 (estimated)
- First posted
- 27 Apr 2022 (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.
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Aim of this study is to investigate the real-time diagnostic performance of AI4CRP for the classification of diminutive colorectal polyps, and to compare it with the real-time diagnostic performance of commercially available CADx systems.
Detailed description
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Despite additional training, even experienced endoscopists continue to fail meeting international thresholds set for safe implementation of treatment strategies based on optical diagnosis.
Multiple machine learning techniques - computer-aided diagnosis (CADx) systems - have been developed for applications in medical imaging within colonoscopy and can improve endoscopic classification of colorectal polyps.
Aim of this study is to explore the feasibility of the workflow using AI4CRP (a CNN based CADx system) real-time in the endoscopy suite, and to investigate the real-time diagnostic performance of AI4CRP for the diagnosis of diminutive (<5mm) colorectal polyps. Secondary, the real-time performance of commercially available CADx systems will be investigated and compared with AI4CRP performance.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Gastroenterology patients | – | Device: Computer-aided diagnosis (CADx) systemsPatient receiving a colonoscopy because of regular care will be considered eligible for inclusion if at least one diminutive colorectal polyp is encountered during the colonoscopy. Patients receive an endoscopic procedure in the context of the Dutch national screening program, because of gastrointestinal symptoms, or because of follow-up of previously diagnosed bowel diseases. Colonoscopies will be executed using Fujifilm endoscopy systems (Fujifilm® Corporation, Tokyo, Japan), using Pentax endoscopy systems (Pentax Medical®, Hamburg, Germany), and using Olympus endoscopy systems (Olympus®, Tokyo, Japan). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patient receiving a colonoscopy because of regular care will be considered eligible for inclusion if at least one diminutive colorectal polyp is encountered during the colonoscopy. Patients receive an endoscopic procedure in the context of the Dutch national screening program, because of gastrointestinal symptoms, or because of follow-up of previously diagnosed bowel diseases.
Key inclusion criteria (4 listed)
- Age >18 years;
- Patients with at least one colorectal polyps encountered during colonoscopy;
- Patients referred for a colonoscopy by the Dutch bowel cancer screening program, patients undergoing a colonoscopy for endoscopic surveillance, or patients undergoing a colonoscopy because of complaints;
- Written informed consent.
Key exclusion criteria (4 listed)
- Patients with prior history of inflammatory bowel diseases (IBD) or polyposis syndromes;
- Patients with inadequate bowel preparations after adequate washing, suctioning, and cleaning manoeuvres have been performed by the endoscopist;
- Patients undergoing an emergency colonoscopy;
- Written objection in the patient file for participation in scientific research.
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-aided diagnosis (CADx) systemsAlso: AI4CRP, artificial intelligence for colorectal polyps (COMET-OPTICAL research group), CAD EYE (Fujifilm® Corporation, Tokyo, Japan)* AI4CRP (artificial intelligence for colorectal polyps), a CNN based computer-aided diagnosis system for diagnosis of colorectal polyps (COMET-OPTICAL research group); * CAD EYE, a computer-aided diagnosis system for diagnosis of colorectal polyps (Fujifilm® Corporation, Tokyo, Japan). | Device | Gastroenterology patients |
Primary outcome
Technical feasibility of real-time use of AI4CRP.
Time frame: 6 months
The technical feasibility of real-time use of AI4CRP in the endoscopy suite regarding a proper reception of the video output from the local endoscopy processor towards AI4CRP (in high definition quality, without any delays in time).
User interface feasibility of real-time use of AI4CRP.
Time frame: 6 months
The user interface feasibility of real-time use of AI4CRP in the endoscopy suite regarding a correct alignment of the user interface of AI4CRP with the video output from the local endoscopy system (resizing image pixels and anonymization).
The diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The real-time diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan). Diagnostic accuracy defined as the percentage of correctly optically diagnosed colorectal polyps.
The sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The real-time sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The real-time specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The real-time negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The real-time positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The real-time Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
21 secondary outcomes
The diagnostic accuracy of AI4CRP per polyp.
Time frame: 1 year
The sensitivity of AI4CRP per polyp.
Time frame: 1 year
The specificity of AI4CRP per polyp.
Time frame: 1 year
The negative predictive value of AI4CRP per polyp.
Time frame: 1 year
The positive predictive value of AI4CRP per polyp.
Time frame: 1 year
The Area Under ROC Curve (AUC) of AI4CRP per polyp.
Time frame: 1 year
The diagnostic accuracy of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The sensitivity of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The specificity of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The negative predictive value of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The positive predictive value of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The diagnostic accuracy of AI4CRP per patient.
Time frame: 1 year
The diagnostic accuracy of CAD EYE per patient.
Time frame: 1 year
The localization score of AI4CRP.
Time frame: 1 year
The difference in diagnostic accuracy of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in sensitivity of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in specificity of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in negative predictive value of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in positive predictive value of endoscopists per polyp before and after AI.
Time frame: 1 year
The agreement in surveillance interval based on optical diagnosis and histopathology.
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 May 2022.
- Individual participant dataSharing planned: Undecided. A data sharing plan is not yet decided on.
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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.
Erik Schoon, Prof Dr MD principal investigator
Maastricht Universitair Medisch Centrum
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Neoplasms and Colorectal Polyp, run by Maastricht University Medical Center at 2 sites in Netherlands.
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 4 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: status not confirmed, as of the sponsor's last update on 5 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Netherlands. 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 Quirine van der Zander, Drs MD (031433882241, q.vanderzander@maastrichtuniversity.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Technical feasibility of real-time use of AI4CRP. (time frame: 6 months). The study lists 8 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 5 May 2022.