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Computer-aided Detection During Screening Colonoscopy

Primary Purpose

Colorectal Polyp, Colorectal Cancer, Colorectal Adenoma

Status
Recruiting
Phase
Not Applicable
Locations
Ecuador
Study Type
Interventional
Intervention
HD- colonoscopy
HD-colonoscopy assisted by AI
Sponsored by
Instituto Ecuatoriano de Enfermedades Digestivas
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Colorectal Polyp focused on measuring Artificial intelligence, Colonoscopy, colorectal cancer

Eligibility Criteria

45 Years - 89 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: Adults ≥45 years old Patients referred for screening colonoscopy Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8 Patients who authorized for endoscopic approach. Exclusion Criteria: 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

Sites / Locations

  • Instituto Ecuatoriano de Enfermedades Digestivas (IECED)Recruiting

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Experimental

Arm Label

HD-colonoscopy + AI-HD colonoscopy

AI-HD colonoscopy + HD-colonoscopy

Arm Description

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.

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.

Outcomes

Primary Outcome Measures

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.
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.
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.

Secondary Outcome Measures

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.

Full Information

First Posted
February 9, 2023
Last Updated
September 26, 2023
Sponsor
Instituto Ecuatoriano de Enfermedades Digestivas
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1. Study Identification

Unique Protocol Identification Number
NCT05734820
Brief Title
Computer-aided Detection During Screening Colonoscopy
Official Title
Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial
Study Type
Interventional

2. Study Status

Record Verification Date
September 2023
Overall Recruitment Status
Recruiting
Study Start Date
January 11, 2020 (Actual)
Primary Completion Date
June 11, 2024 (Anticipated)
Study Completion Date
September 1, 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Instituto Ecuatoriano de Enfermedades Digestivas

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No

5. Study Description

Brief 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. 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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Colorectal Polyp, Colorectal Cancer, Colorectal Adenoma
Keywords
Artificial intelligence, Colonoscopy, colorectal cancer

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Crossover Assignment
Model Description
Blinded, single center, controlled, prospective trial
Masking
Care Provider
Allocation
Non-Randomized
Enrollment
312 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
HD-colonoscopy + AI-HD colonoscopy
Arm Type
Experimental
Arm Description
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.
Arm Title
AI-HD colonoscopy + HD-colonoscopy
Arm Type
Experimental
Arm Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
HD- colonoscopy
Intervention Description
HD-colonoscopy performed by an expert or non-expert endoscopist. All lesions will be recorded, assessed, and removed for histological analysis.
Intervention Type
Diagnostic Test
Intervention Name(s)
HD-colonoscopy assisted by AI
Intervention Description
HD-colonoscopy with AI-assisted polyp detector. New polyps detected by AI will be recorded, removed, and studied.
Primary Outcome Measure Information:
Title
Adenoma detection rate (ADR)
Description
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.
Time Frame
up to one month
Title
Polyp detection rate (PDR)
Description
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.
Time Frame
up to two hours
Title
Diagnostic performance of AI-assisted polyp detector
Description
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.
Time Frame
up to three years
Secondary Outcome Measure Information:
Title
Adenoma Miss Rate (AMR)
Description
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.
Time Frame
Up to one month

10. Eligibility

Sex
All
Minimum Age & Unit of Time
45 Years
Maximum Age & Unit of Time
89 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: Adults ≥45 years old Patients referred for screening colonoscopy Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8 Patients who authorized for endoscopic approach. Exclusion Criteria: 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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Carlos Robles-Medranda, MD FASGE
Phone
+59342109180
Email
carlosoakm@yahoo.es
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Carlos Robles-Medranda, MD FASGE
Organizational Affiliation
Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
Official's Role
Principal Investigator
Facility Information:
Facility Name
Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
City
Guayaquil
State/Province
Guayas
ZIP/Postal Code
090505
Country
Ecuador
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Carlos Robles-Medranda, MD FASGE
Phone
+59342109180
Email
carlosoakm@yahoo.es
First Name & Middle Initial & Last Name & Degree
Hannah Pitanga-Lukashok, MD
First Name & Middle Initial & Last Name & Degree
Maria Egas-Izquierdo, MD
First Name & Middle Initial & Last Name & Degree
Carlos Cifuentes-Gordillo, MD
First Name & Middle Initial & Last Name & Degree
Miguel Puga-Tejada, MD
First Name & Middle Initial & Last Name & Degree
Jorge Baquerizo-Burgos, MD
First Name & Middle Initial & Last Name & Degree
Domenica Cunto, MD
First Name & Middle Initial & Last Name & Degree
Martha Arevalo-Mora, MD
First Name & Middle Initial & Last Name & Degree
Juan Alcivar-Vasquez, MD
First Name & Middle Initial & Last Name & Degree
Raquel Del Valle, MD
First Name & Middle Initial & Last Name & Degree
Haydee Alvarado-Escobar, MD
First Name & Middle Initial & Last Name & Degree
Daniela Tabacelia, MD
First Name & Middle Initial & Last Name & Degree
Carlos Robles-Medranda, MD FASGE

12. IPD Sharing Statement

Citations:
PubMed Identifier
30814121
Citation
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.
Results Reference
background
PubMed Identifier
24693890
Citation
Corley 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.
Results Reference
background
PubMed Identifier
34790008
Citation
Kroner 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.
Results Reference
background
PubMed Identifier
34263163
Citation
Parsa 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.
Results Reference
background
PubMed Identifier
31981518
Citation
Gong 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. Erratum In: Lancet Gastroenterol Hepatol. 2020 Apr;5(4):e3.
Results Reference
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Computer-aided Detection During Screening Colonoscopy

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