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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| 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 |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Colonoscopy | Procedure | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 7 days after colonoscopy (estimated time for histopathological report release). |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Márcio Roberto Facanali Júnior | Contact | +55 19 99825-2870 | marcio.facanali@hc.fm.usp.br | |
| Adriana Vaz Safatle Ribeiro, PhD | Contact | +55 19 99825-2870 |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital das Clínicas da Faculdade de Medicina da USP | Recruiting | São Paulo | São Paulo | 05403-010 | Brazil |
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| Label | URL |
|---|---|
| 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. | View source |
| Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, et al. Adenoma Detection Rate and Risk of Colorectal Cancer and Death. N Engl J Med \[Internet\]. 2014 Apr 3;370(14):1298-306 | View source |
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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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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Sep 8, 2022 | Jul 4, 2025 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| D011125 | Adenomatous Polyposis Coli |
| D000236 | Adenoma |
| ID | Term |
|---|---|
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
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| ID | Term |
|---|---|
| D003113 | Colonoscopy |
| ID | Term |
|---|---|
| D016099 | Endoscopy, Gastrointestinal |
| D016145 | Endoscopy, Digestive System |
| D003938 | Diagnostic Techniques, Digestive System |
| D019937 | Diagnostic Techniques and Procedures |
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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.
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| 7 days after colonoscopy |
| 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. | View source |
| 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 \[Internet\]. | View source |
| 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. | View source |
| 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):13 | View source |
| 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):4 | View source |
| Kominami Y, Yoshida S, Tanaka S, Sanomura Y, Hirakawa T, Raytchev B, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc \[Internet | View source |
| Kuiper T, Alderlieste Y, Tytgat K, Vlug M, Nabuurs J, Bastiaansen B, et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy \[Internet\]. 2014 Sep 29;47(01):56-62. | View source |
| Rath T, Tontini G, Vieth M, Nägel A, Neurath M, Neumann H. In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy. Endoscopy \[Internet\]. | View source |
| Keats AS. The ASA Classification of Physical Status-A Recapitulation. Anesthesiology \[Internet\]. 1978 Oct 1;49(4):233-5. | View source |
| Calderwood AH, Jacobson BC. Comprehensive validation of the Boston Bowel Preparation Scale. Gastrointest Endosc \[Internet\]. 2010 Oct;72(4):686-92 | View source |
| Chernik DA, Gillings D, Laine H, Hendler J, Silver JM, Davidson AB, et al. Validity and reliability of the Observer's Assessment of Alertness/Sedation Scale: study with intravenous midazolam. J Clin Psychopharmacol \[Internet\]. 1990 Aug;10(4):244-51 | View source |
| Kudo S, Hirota S, Nakajima T, Hosobe S, Kusaka H, Kobayashi T, et al. Colorectal tumours and pit pattern. J Clin Pathol \[Internet\]. 1994 Oct 1;47(10):880-5. | View source |
| Kimura T, Yamamoto E, Yamano H, Suzuki H, Kamimae S, Nojima M, et al. A Novel Pit Pattern Identifies the Precursor of Colorectal Cancer Derived From Sessile Serrated Adenoma. Am J Gastroenterol \[Internet\]. 2012 Mar;107(3):460- 9 | View source |
| Participants in the Paris Workshop. The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon. Gastrointest Endosc \[Internet\]. 2003 Dec;58(6):S3-43. | View source |
| Dixon MF. Gastrointestinal epithelial neoplasia: Vienna revisited. Gut \[Internet\]. 2002 Jul 1;51(1):130-1. | View source |
| Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform \[Internet\]. 2019 Jul;95:103208 | View source |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
| D018256 | Adenomatous Polyps |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009386 | Neoplastic Syndromes, Hereditary |
| D044483 | Intestinal Polyposis |
| D030342 | Genetic Diseases, Inborn |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D003933 | Diagnosis |
| D004724 | Endoscopy |
| D003949 | Diagnostic Techniques, Surgical |
| D013505 | Digestive System Surgical Procedures |
| D013514 | Surgical Procedures, Operative |
| D019060 | Minimally Invasive Surgical Procedures |