Colorectal PolypNCT05734820
Computer-aided Detection During Screening Colonoscopy
A study testing HD- colonoscopy and HD-colonoscopy assisted by AI in people with Colorectal Polyp and Colorectal Cancer, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
Trial summary
Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Read more about this study
- Who is it for?
- Ages 45 to 89 years
- Conditions the study is for
- Colorectal Polyp, Colorectal Cancer, Colorectal Adenoma
- What is being tested?
- HD- colonoscopy and HD-colonoscopy assisted by AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 312 participants
- Where
- 1 siteEcuador
- Study dates
- 11 Jan 2020to 1 Sep 2024
- 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 45 to 89 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Adults ≥45 years old
- Patients referred for screening colonoscopy
- Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8
- Patients who authorized for endoscopic approach.
Who cannot join
- Pregnancy
- Any clinical condition which makes endoscopy inviable.
- Patients with history of Colorectal Carcinoma.
- Patients with history of Inflammatory Bowel Disease (IBD)
- Inability to provide informed consent
Trial locations and contacts
1 site in 1 country.
Ecuador1 site, 1 recruiting
- Recruiting
Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
Guayaquil, Guayas, 090505
Carlos Robles-Medranda, MD FASGE, +59342109180, carlosoakm@yahoo.es
Hannah Pitanga-Lukashok, MD
About this study
Official title: Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial
Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR).
Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.
Read the detailed description
Colorectal cancer (CRC) is worldwide the second and third cancer-related cause of death in men and women, respectively. For the detection of lesions in the mucosa (premalignant and malignant), colonoscopy has been considered the gold standard. However, up to 25% of lesions can be missed during conventional colonoscopy. Some technical (i.e., bowel preparation) and operator-related (i.e., expertise, and fatigue) factors are related to these missing lesions.
During the rapid-growing technological era, new tools were launched to improve the quality and performance of colonoscopies. Through the assistance of artificial intelligence (AI) an identification of a pattern can be achieved after a previous training from a large dataset of images. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan), is a computer-assisted polyp/adenoma detection system based on AI. It detects classic adenomas and flat lesions, distinguished features like mucus cap or rim of debris with the advantage of a real-time and simultaneous multiple polyp detection. It was developed to minimize the missed lesions increasing as a result the polyp detection rate (PDR) and the adenoma detection rate (ADR).
Lately, published data evaluating the AI-assisted polyp detectors has demonstrate high sensitivity, specificity, and interobserver agreement. Due to the importance of CRC diagnosis and prompt treatment, and taking advantage of the newly introduced DiscoveryTM AI system, the investigators aim to assess the real-world effectiveness of this AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.
- Conditions
- Colorectal Polyp, Colorectal Cancer, Colorectal Adenoma
- Being tested
- HD- colonoscopy, HD-colonoscopy assisted by AI
- Keywords
- Artificial intelligence, Colonoscopy, colorectal cancer
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
HD- colonoscopyDiagnostic test
HD-colonoscopy performed by an expert or non-expert endoscopist. All lesions will be recorded, assessed, and removed for histological analysis.
HD-colonoscopy assisted by AIDiagnostic test
HD-colonoscopy with AI-assisted polyp detector. New polyps detected by AI will be recorded, removed, and studied.
| Study group | Role | What they receive |
|---|---|---|
| HD-colonoscopy + AI-HD colonoscopy | Experimental | This group is comprised by patients >45 years of age submitted for diagnostic colonoscopy. In the same session a HD-colonoscopy will be performed followed by an HD-colonoscopy with artificial intelligence assistance. The second procedure will be performed by an operator with the same-level-of -expertise in comparison to the initial procedure (expert or non-expert) and blinded to the results of the previous intervention. Receives: Diagnostic Test: HD- colonoscopy; Diagnostic Test: HD-colonoscopy assisted by AI |
| AI-HD colonoscopy + HD-colonoscopy | Experimental | This group is comprised by patients >45 years of age submitted for diagnostic colonoscopy. In the same session a HD-colonoscopy assisted by artificial intelligence will be performed followed by an HD-colonoscopy alone.The second procedure will be performed by an operator with the same-level-of -expertise in comparison to the initial procedure (expert or non-expert) and blinded to the results of the previous intervention. Receives: Diagnostic Test: HD- colonoscopy; Diagnostic Test: HD-colonoscopy assisted by AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Adenoma detection rate (ADR)
The ADR will be determined by every new colonoscopy (second intervention) with at least one adenoma, histologically proven/NBI NICE classification.
Results will be compared between experts and non-experts endoscopists.
Measured: up to one month
Polyp detection rate (PDR)
The PDR will be determined by every new colonoscopy (second intervention) with at least one polyp.
Results will be compared between experts and non-experts endoscopists.
Measured: up to two hours
Diagnostic performance of AI-assisted polyp detector
The diagnostic performance of the AI-assisted system will be assessed by sensitivity, specificity, positive and negative predictive values (PPV and NPV) and observer agreement.
Measured: up to three years
Secondary outcomes (1)
Adenoma Miss Rate (AMR)
The AMR will be determined by the total number of missed adenomas on initial examination. The diagnosis of adenoma will be made by NBI NICE classification or biopsy.
Measured: Up to one month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2023
- Study start11 Jan 2020
- Primary completion11 Jun 2024 (estimated)
- Study completion1 Sep 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Colorectal 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
- Crossover
- Model description
- Blinded, single center, controlled, prospective trial
- Masking
- Single blind
- Who is masked
- Care provider
- Enrolment
- 312 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades Digestivas
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Carlos Robles-Medranda, Instituto Ecuatoriano de Enfermedades Digestivas
- Investigators
- Carlos Robles-Medranda, MD FASGE, Principal investigator, Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05734820
- Sponsor's protocol ID
- IECED-01062023
- Status last verified
- Sep 2023
- First submitted
- 9 Feb 2023
- Last updated
- 28 Sep 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27.
PubMed 30814121Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014 Apr 3;370(14):1298-306. doi: 10.1056/NEJMoa1309086.
PubMed 24693890Kroner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol. 2021 Oct 28;27(40):6794-6824. doi: 10.3748/wjg.v27.i40.6794.
PubMed 34790008Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc. 2021 Jun 29;14:26317745211014698. doi: 10.1177/26317745211014698. eCollection 2021 Jan-Dec.
PubMed 34263163Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):352-361. doi: 10.1016/S2468-1253(19)30413-3. Epub 2020 Jan 22.
PubMed 31981518
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial
- ClinicalTrials.gov ID
- NCT05734820
- Acronym / sponsor ID
- IECED-01062023
- Conditions
- Colorectal Polyp, Colorectal Cancer, Colorectal Adenoma
- Keywords
- Artificial intelligence, Colonoscopy, colorectal cancer
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 312 participants (estimated)
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades DigestivasUniversity, hospital or other
- Study start
- 11 Jan 2020 (actual)
- Primary completion
- 11 Jun 2024 (estimated)
- Study completion
- 1 Sep 2024 (estimated)
- First posted
- 21 Feb 2023 (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.
Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR).
Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.
Detailed description
Colorectal cancer (CRC) is worldwide the second and third cancer-related cause of death in men and women, respectively. For the detection of lesions in the mucosa (premalignant and malignant), colonoscopy has been considered the gold standard. However, up to 25% of lesions can be missed during conventional colonoscopy. Some technical (i.e., bowel preparation) and operator-related (i.e., expertise, and fatigue) factors are related to these missing lesions.
During the rapid-growing technological era, new tools were launched to improve the quality and performance of colonoscopies. Through the assistance of artificial intelligence (AI) an identification of a pattern can be achieved after a previous training from a large dataset of images. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan), is a computer-assisted polyp/adenoma detection system based on AI. It detects classic adenomas and flat lesions, distinguished features like mucus cap or rim of debris with the advantage of a real-time and simultaneous multiple polyp detection. It was developed to minimize the missed lesions increasing as a result the polyp detection rate (PDR) and the adenoma detection rate (ADR).
Lately, published data evaluating the AI-assisted polyp detectors has demonstrate high sensitivity, specificity, and interobserver agreement. Due to the importance of CRC diagnosis and prompt treatment, and taking advantage of the newly introduced DiscoveryTM AI system, the investigators aim to assess the real-world effectiveness of this AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Crossover
- Masking
- Single blind (care provider)
- Number of arms
- 2
- Model description
- Blinded, single center, controlled, prospective trial
| Arm | Type | Receives |
|---|---|---|
| HD-colonoscopy + AI-HD colonoscopy | Experimental | Diagnostic Test: HD- colonoscopy, Diagnostic Test: HD-colonoscopy assisted by AIThis group is comprised by patients >45 years of age submitted for diagnostic colonoscopy. In the same session a HD-colonoscopy will be performed followed by an HD-colonoscopy with artificial intelligence assistance. The second procedure will be performed by an operator with the same-level-of -expertise in comparison to the initial procedure (expert or non-expert) and blinded to the results of the previous intervention. |
| AI-HD colonoscopy + HD-colonoscopy | Experimental | Diagnostic Test: HD- colonoscopy, Diagnostic Test: HD-colonoscopy assisted by AIThis group is comprised by patients >45 years of age submitted for diagnostic colonoscopy. In the same session a HD-colonoscopy assisted by artificial intelligence will be performed followed by an HD-colonoscopy alone.The second procedure will be performed by an operator with the same-level-of -expertise in comparison to the initial procedure (expert or non-expert) and blinded to the results of the previous intervention. |
Who may qualify
- Ages
- 45 to 89 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Adults ≥45 years old
- Patients referred for screening colonoscopy
- Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8
- Patients who authorized for endoscopic approach.
Key exclusion criteria (5 listed)
- Pregnancy
- Any clinical condition which makes endoscopy inviable.
- Patients with history of Colorectal Carcinoma.
- Patients with history of Inflammatory Bowel Disease (IBD)
- Inability to provide informed consent
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 |
|---|---|---|
| HD- colonoscopyHD-colonoscopy performed by an expert or non-expert endoscopist. All lesions will be recorded, assessed, and removed for histological analysis. | Diagnostic test | AI-HD colonoscopy + HD-colonoscopy, HD-colonoscopy + AI-HD colonoscopy |
| HD-colonoscopy assisted by AIHD-colonoscopy with AI-assisted polyp detector. New polyps detected by AI will be recorded, removed, and studied. | Diagnostic test | AI-HD colonoscopy + HD-colonoscopy, HD-colonoscopy + AI-HD colonoscopy |
Primary outcome
Adenoma detection rate (ADR)
Time frame: up to one month
The ADR will be determined by every new colonoscopy (second intervention) with at least one adenoma, histologically proven/NBI NICE classification. Results will be compared between experts and non-experts endoscopists.
Polyp detection rate (PDR)
Time frame: up to two hours
The PDR will be determined by every new colonoscopy (second intervention) with at least one polyp. Results will be compared between experts and non-experts endoscopists.
Diagnostic performance of AI-assisted polyp detector
Time frame: up to three years
The diagnostic performance of the AI-assisted system will be assessed by sensitivity, specificity, positive and negative predictive values (PPV and NPV) and observer agreement.
1 secondary outcome
Adenoma Miss Rate (AMR)
Time frame: Up to one month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Sep 2023.
- Background: PubMed 30814121Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27.
- Background: PubMed 24693890Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014 Apr 3;370(14):1298-306. doi: 10.1056/NEJMoa1309086.
- Background: PubMed 34790008Kroner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol. 2021 Oct 28;27(40):6794-6824. doi: 10.3748/wjg.v27.i40.6794.
- Background: PubMed 34263163Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc. 2021 Jun 29;14:26317745211014698. doi: 10.1177/26317745211014698. eCollection 2021 Jan-Dec.
- Background: PubMed 31981518Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):352-361. doi: 10.1016/S2468-1253(19)30413-3. Epub 2020 Jan 22.
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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.
Carlos Robles-Medranda, MD FASGE principal investigator
Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing HD- colonoscopy and HD-colonoscopy assisted by AI in people with Colorectal Polyp and Colorectal Cancer, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
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 5 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 28 Sep 2023. 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 Ecuador. 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 Carlos Robles-Medranda, MD FASGE (+59342109180, carlosoakm@yahoo.es), 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: up to one month). The study lists 3 primary outcomes in all. 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 NCT05734820, which also lists 5 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 28 Sep 2023.