Colorectal Cancer (CRC)NCT07066046
Artificial Intelligence-assisted Colonoscopy in the Detection and Characterization of Colorectal Lesions
A study testing Colonoscopy in people with Colorectal Cancer (CRC) and Adenomatous Polyposis, run by Instituto do Cancer do Estado de São Paulo at 1 site in Brazil.
Trial summary
The study aims to evaluate the effectiveness of artificial intelligence-assisted colonoscopy in increasing adenoma detection rate and the accuracy in the characterization of colorectal lesions, compared to standard colonoscopy, in a randomized controlled clinical trial setting. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Colorectal Cancer (CRC), Adenomatous Polyposis, Colorectal Lesions
- What is being tested?
- Colonoscopy
- Type of treatment
- Surgery or procedure
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteBrazil
- Study dates
- 1 Feb 2025to 1 Dec 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- All patients aged 18 years or older, with an elective indication for colonoscopy who sign the informed consent form agreeing to participate in the study.
Who cannot join
- History of inflammatory bowel disease.
- History of colorectal cancer.
- Personal history of colorectal surgery.
- Contraindication to endoscopic biopsies.
- History of intestinal polyposis syndromes.
- Urgent or emergency cases.
Show all 10Show fewer
- Presence of severe, decompensated comorbidities, or with a score of 3 or higher according to the American Society of Anesthesiologists (ASA) classification.
- Incomplete colonoscopy that does not reach the cecum.
- Insufficient or inadequate bowel preparation, with a score lower than 6 on the Boston Bowel Preparation Scale.
- Patients who do not agree to participate in the study and do not sign the informed consent form (ICF).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Adriana Vaz Safatle Ribeiro, PhD contact
Brazil1 site, 1 recruiting
- Recruiting
Hospital das Clínicas da Faculdade de Medicina da USP
São Paulo, São Paulo, 05403-010
Márcio R Facanali Júnior
About this study
Official title: Artificial Intelligence-assisted Colonoscopy in the Detection and Characterization of Colorectal Lesions: Randomized Controlled Clinical Trial
The study aims to evaluate the effectiveness of artificial intelligence-assisted colonoscopy in increasing adenoma detection rate and the accuracy in the characterization of colorectal lesions, compared to standard colonoscopy, in a randomized controlled clinical trial setting.
Read the detailed description
Colorectal cancer (CRC) currently shows, according to GLOBOCAN, an incidence of 19.5 individuals per 100,000 inhabitants in both sexes, being the third most common cancer in men and the second in women, representing the third leading cause of death in both men and women.
According to the GLOBOCAN registry of the World Health Organization (WHO), it is estimated that CRC is the third most common type of cancer worldwide, responsible for 10% of all newly diagnosed cancer cases, corresponding to 1,931,590 cases in 2020, preceded only by lung cancer (11.4%) and breast cancer (11.7%). CRC is the second leading cause of cancer mortality (9.4%; 935,173 cases in 2020), following only lung cancer, which accounts for 18% of cancer deaths globally.
In Brazil, according to data from the National Cancer Institute (INCA), CRC mirrors the global incidence, being the second most common cancer by sex.
Colonoscopy is the most accurate CRC screening method, with sensitivity reaching 100% in the detection of colorectal lesions. According to studies, for each 1% increase in adenoma detection rate, there is a 5% decrease in CRC mortality, highlighting the importance of performing colonoscopy to detect colorectal lesions, especially adenomas.
Consequently, with the advancement of technology, new high-definition endoscopes with virtual chromoscopy and image magnification have been developed to increase adenoma detection rates. More recently, AI-assisted colonoscopy has been gaining prominence in helping prevent CRC in some medical centers worldwide, such as in Japan.
In a multicenter study with 700 patients in 2019, a significantly higher adenoma detection rate was demonstrated with AI-assisted colonoscopy compared to standard colonoscopy (54.8% vs. 40.4%). Subsequently, a randomized, double-blind clinical trial with 1,058 patients was conducted, comparing standard colonoscopy to AI-assisted colonoscopy. The result was an adenoma detection rate of 29% for AI-assisted colonoscopy and 20% for standard colonoscopy, with the difference being statistically significant. Two other studies comparing AI-assisted colonoscopy and standard colonoscopy showed similar results.
However, when analyzing the accuracy of AI systems in characterizing colorectal lesions, different results are observed in the literature. On one hand, Japanese studies report accuracies above 90% in characterizing neoplastic and non-neoplastic lesions with artificial intelligence, while other studies, such as the Dutch study and the German study, found accuracies of 74.4% and 84.7%, respectively, results significantly lower compared to the Japanese studies.
Therefore, given not only the differences in results obtained by various authors but also the differences in population and the lack of studies on AI-assisted colonoscopy in developing countries, the objective of this work is to evaluate the adenoma detection rate of AI-assisted colonoscopy and assess the accuracy of artificial intelligence in characterizing colorectal lesions.
- Being tested
- Colonoscopy
- Keywords
- Adenoma, Artificial Intelligence, Colonoscopy, Colorectal lesions
- Medical subject headings
- Colorectal Neoplasms, Adenomatous Polyposis Coli, Adenoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ColonoscopyProcedure or surgery
This single-center, randomized, open-label clinical trial will assess the effectiveness of artificial intelligence (AI)-assisted colonoscopy versus standard high-definition colonoscopy in detecting and characterizing colorectal lesions. Conducted over 12 months in São Paulo, Brazil, the study will include 100 adult patients undergoing elective colonoscopy.
Participants will be stratified by age and randomized (1:1) after sedation. All lesions will be resected, recorded, and analyzed histologically. The intervention group will also include AI output data (CAD EYE).
The primary goals are to evaluate adenoma detection rate (ADR) and AI diagnostic accuracy. Given the global burden of colorectal cancer (CRC), particularly in developing countries, this study aims to provide real-world data on the impact of AI in CRC screening.
| Study group | Role | What they receive |
|---|---|---|
| Colonoscopy with the aid of artificial intelligence | Active comparator | Stratum 1: Patients aged 18 to 44 years
Stratum 2: Patients aged 45 to 75 years
Stratum 3: Patients aged 76 years or older Receives: Procedure: Colonoscopy |
| Colonoscopy without the aid of artificial intelligence | Active comparator | Stratum 1: Patients aged 18 to 44 years
Stratum 2: Patients aged 45 to 75 years
Stratum 3: Patients aged 76 years or older Receives: Procedure: Colonoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of patients with at least one adenoma detected, confirmed by histopathological analysis, during colonoscopy, in the AI group vs. control group
The measure will be expressed as the number and percentage (%) of patients with at least one adenoma detected during colonoscopy and confirmed by histopathological analysis, comparing the AI and non-AI groups (CAD EYE). Detection will be based on the analysis of biopsies performed and processed according to the standard protocol.
Measured: 7 days after colonoscopy (estimated time for histopathological report release).
Secondary outcomes (1)
Diagnostic accuracy of CAD EYE for characterization of lesions as neoplastic (adenoma) or non-neoplastic (hyperplastic), compared to histopathological analysis as the gold standard.
The accuracy of artificial intelligence (CAD EYE) in characterizing detected lesions as neoplastic or non-neoplastic will be calculated, based on comparison with histopathological diagnosis (gold standard). The following will be reported: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), in percentage (%), for each type of lesion.
Measured: 7 days after colonoscopy
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jul 2025
- Study start1 Feb 2025
- Primary completion1 Dec 2026 (estimated)
- Study completion1 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal Cancer (CRC)Studies 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
- Randomised
- Intervention model
- Parallel groups
- Model description
- A controlled, open-label, prospective, randomized clinical study is proposed, to be conducted at a single center in a Brazilian referral hospital for colorectal cancer located in São Paulo, São Paulo. Over a period of 12 consecutive months, patients who agree to participate in the study will undergo a colonoscopy. All patients aged 18 years or older, with an elective indication for colonoscopy, who sign the informed consent form agreeing to participate in the study, will be included.
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Instituto do Cancer do Estado de São Paulo
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, ADRIANA VAZ SAFATLE RIBEIRO, Instituto do Cancer do Estado de São Paulo
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07066046
- Sponsor's protocol ID
- 64060322.7.0000.0068
- Status last verified
- Jul 2025
- First submitted
- 22 May 2025
- Last updated
- 23 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We do not plan to share Individual Participant Data (IPD) with other researchers due to privacy concerns and the nature of the study
Study at a glance
From the registry record, last updated by the sponsor on 23 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted Colonoscopy in the Detection and Characterization of Colorectal Lesions: Randomized Controlled Clinical Trial
- ClinicalTrials.gov ID
- NCT07066046
- Acronym / sponsor ID
- 64060322.7.0000.0068
- Keywords
- Adenoma, Artificial Intelligence, Colonoscopy, Colorectal lesions
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Instituto do Cancer do Estado de São PauloUniversity, hospital or other
- Study start
- 1 Feb 2025 (actual)
- Primary completion
- 1 Dec 2026 (estimated)
- Study completion
- 1 Dec 2026 (estimated)
- First posted
- 15 Jul 2025 (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 study aims to evaluate the effectiveness of artificial intelligence-assisted colonoscopy in increasing adenoma detection rate and the accuracy in the characterization of colorectal lesions, compared to standard colonoscopy, in a randomized controlled clinical trial setting.
Detailed description
Colorectal cancer (CRC) currently shows, according to GLOBOCAN, an incidence of 19.5 individuals per 100,000 inhabitants in both sexes, being the third most common cancer in men and the second in women, representing the third leading cause of death in both men and women.
According to the GLOBOCAN registry of the World Health Organization (WHO), it is estimated that CRC is the third most common type of cancer worldwide, responsible for 10% of all newly diagnosed cancer cases, corresponding to 1,931,590 cases in 2020, preceded only by lung cancer (11.4%) and breast cancer (11.7%). CRC is the second leading cause of cancer mortality (9.4%; 935,173 cases in 2020), following only lung cancer, which accounts for 18% of cancer deaths globally.
In Brazil, according to data from the National Cancer Institute (INCA), CRC mirrors the global incidence, being the second most common cancer by sex.
Colonoscopy is the most accurate CRC screening method, with sensitivity reaching 100% in the detection of colorectal lesions. According to studies, for each 1% increase in adenoma detection rate, there is a 5% decrease in CRC mortality, highlighting the importance of performing colonoscopy to detect colorectal lesions, especially adenomas.
Consequently, with the advancement of technology, new high-definition endoscopes with virtual chromoscopy and image magnification have been developed to increase adenoma detection rates. More recently, AI-assisted colonoscopy has been gaining prominence in helping prevent CRC in some medical centers worldwide, such as in Japan.
In a multicenter study with 700 patients in 2019, a significantly higher adenoma detection rate was demonstrated with AI-assisted colonoscopy compared to standard colonoscopy (54.8% vs. 40.4%). Subsequently, a randomized, double-blind clinical trial with 1,058 patients was conducted, comparing standard colonoscopy to AI-assisted colonoscopy. The result was an adenoma detection rate of 29% for AI-assisted colonoscopy and 20% for standard colonoscopy, with the difference being statistically significant. Two other studies comparing AI-assisted colonoscopy and standard colonoscopy showed similar results.
However, when analyzing the accuracy of AI systems in characterizing colorectal lesions, different results are observed in the literature. On one hand, Japanese studies report accuracies above 90% in characterizing neoplastic and non-neoplastic lesions with artificial intelligence, while other studies, such as the Dutch study and the German study, found accuracies of 74.4% and 84.7%, respectively, results significantly lower compared to the Japanese studies.
Therefore, given not only the differences in results obtained by various authors but also the differences in population and the lack of studies on AI-assisted colonoscopy in developing countries, the objective of this work is to evaluate the adenoma detection rate of AI-assisted colonoscopy and assess the accuracy of artificial intelligence in characterizing colorectal lesions.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
- Model description
- A controlled, open-label, prospective, randomized clinical study is proposed, to be conducted at a single center in a Brazilian referral hospital for colorectal cancer located in São Paulo, São Paulo. Over a period of 12 consecutive months, patients who agree to participate in the study will undergo a colonoscopy. All patients aged 18 years or older, with an elective indication for colonoscopy, who sign the informed consent form agreeing to participate in the study, will be included.
| Arm | Type | Receives |
|---|---|---|
| Colonoscopy with the aid of artificial intelligence | Active comparator | Procedure: ColonoscopyStratum 1: Patients aged 18 to 44 years Stratum 2: Patients aged 45 to 75 years Stratum 3: Patients aged 76 years or older |
| Colonoscopy without the aid of artificial intelligence | Active comparator | Procedure: ColonoscopyStratum 1: Patients aged 18 to 44 years Stratum 2: Patients aged 45 to 75 years Stratum 3: Patients aged 76 years or older |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All patients aged 18 years or older, with an elective indication for colonoscopy who sign the informed consent form agreeing to participate in the study.
Key exclusion criteria (10 listed)
- History of inflammatory bowel disease.
- History of colorectal cancer.
- Personal history of colorectal surgery.
- Contraindication to endoscopic biopsies.
- History of intestinal polyposis syndromes.
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 |
|---|---|---|
| ColonoscopyThis single-center, randomized, open-label clinical trial will assess the effectiveness of artificial intelligence (AI)-assisted colonoscopy versus standard high-definition colonoscopy in detecting and characterizing colorectal lesions. Conducted over 12 months in São Paulo, Brazil, the study will include 100 adult patients undergoing elective colonoscopy. Participants will be stratified by age and randomized (1:1) after sedation. All lesions will be resected, recorded, and analyzed histologically. The intervention group will also include AI output data (CAD EYE). The primary goals are to evaluate adenoma detection rate (ADR) and AI diagnostic accuracy. Given the global burden of colorectal cancer (CRC), particularly in developing countries, this study aims to provide real-world data on the impact of AI in CRC screening. | Procedure or surgery | Colonoscopy with the aid of artificial intelligence, Colonoscopy without the aid of artificial intelligence |
Primary outcome
Number of patients with at least one adenoma detected, confirmed by histopathological analysis, during colonoscopy, in the AI group vs. control group
Time frame: 7 days after colonoscopy (estimated time for histopathological report release).
The measure will be expressed as the number and percentage (%) of patients with at least one adenoma detected during colonoscopy and confirmed by histopathological analysis, comparing the AI and non-AI groups (CAD EYE). Detection will be based on the analysis of biopsies performed and processed according to the standard protocol.
1 secondary outcome
Diagnostic accuracy of CAD EYE for characterization of lesions as neoplastic (adenoma) or non-neoplastic (hyperplastic), compared to histopathological analysis as the gold standard.
Time frame: 7 days after colonoscopy
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 23 Jun 2026.
- Shaukat A, Kahi CJ, Burke CA, Rabeneck L, Sauer BG, Rex DK. ACG Clinical Guidelines: Colorectal Cancer Screening 2021. Am J Gastroenterol \[Internet\]. 2021 Mar;116(3):458-79.Link provided by the sponsor.
- Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, et al. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology \[Internet\]. 2020 Aug;159(2):512-520.e7.Link provided by the sponsor.
- Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, et al. Effect of a deeplearning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol \[Internet\].Link provided by the sponsor.
- Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol \[Internet\]. 2020 Apr;5(4):352-61.Link provided by the sponsor.
- Liu W-N, Zhang Y-Y, Bian X-Q, Wang L-J, Yang Q, Zhang X-D, et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol \[Internet\]. 2020;26(1):13Link provided by the sponsor.
- Aihara H, Saito S, Inomata H, Ide D, Tamai N, Ohya TR, et al. Computer-aided diagnosis of neoplastic colorectal lesions using 'real-time' numerical color analysis during autofluorescence endoscopy. Eur J Gastroenterol Hepatol \[Internet\]. 2013 Apr;25(4):4Link provided by the sponsor.
- Individual participant dataSharing planned: No. We do not plan to share Individual Participant Data (IPD) with other researchers due to privacy concerns and the nature of the study
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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.
Adriana Vaz Safatle Ribeiro, PhD contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Colonoscopy in people with Colorectal Cancer (CRC) and Adenomatous Polyposis, run by Instituto do Cancer do Estado de São Paulo at 1 site in Brazil.
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 10 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 23 Jun 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 Brazil. 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 Márcio Roberto Facanali Júnior (+55 19 99825-2870, marcio.facanali@hc.fm.usp.br), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of patients with at least one adenoma detected, confirmed by histopathological analysis, during colonoscopy, in the AI group vs. control group (time frame: 7 days after colonoscopy (estimated time for histopathological report release).). 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 23 Jun 2026.