Computer-aided Detection During Screening Colonoscopy (Experts)
Primary Purpose
Colorectal Neoplasms, Colon Polyp, Colon Adenoma
Status
Recruiting
Phase
Not Applicable
Locations
Ecuador
Study Type
Interventional
Intervention
Standard high-definition colonoscopy
Colonoscopy with real-time AI assisted automated polyp detection
Sponsored by
About this trial
This is an interventional diagnostic trial for Colorectal Neoplasms focused on measuring colonoscopy, colorectal cancer
Eligibility Criteria
Inclusion Criteria:
- Provided informed written consent
- Age greater than 45 years of age
- Adequate Bowel preparation
Exclusion Criteria:
- History of inflammatory bowel disease, familial polyposis syndrome
- History of colorectal carcinoma, colorectal surgery
- History of uncontrolled coagulopathy
- History of previously failed attempt colonoscopy
Sites / Locations
- Ecuadorian Institute of Digestive DiseasesRecruiting
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
Patients for CRC screening and diagnostic colonoscopy
Arm Description
Consecutive patients >45 years of age submitted for diagnostic colonoscopy
Outcomes
Primary Outcome Measures
Adenoma detection rate of computer-aided after standard colonoscopy.
Number of examinations with at least one adenoma detected during colonoscopy while using the AI-based model
Polyp detection rate of computer-aided following standard colonoscopy.
Number of examination with at least one polyp detected while using the AI-based model
Secondary Outcome Measures
Polyp miss rate of standard high-definition colonoscopy.
Total number of missed polyps/ (total number of missed polyps + total number of polyps on initial examination)
Adenoma miss rate of standard high-definition colonoscopy.
Total number of missed adenomas/ (total number of missed adenomas + total number of adenomas on initial examination)
Full Information
NCT ID
NCT04915833
First Posted
June 1, 2021
Last Updated
March 30, 2022
Sponsor
Instituto Ecuatoriano de Enfermedades Digestivas
1. Study Identification
Unique Protocol Identification Number
NCT04915833
Brief Title
Computer-aided Detection During Screening Colonoscopy (Experts)
Official Title
Real-time Computer-aided Polyp Detection During Screening Colonoscopy Performed by Expert Endoscopists
Study Type
Interventional
2. Study Status
Record Verification Date
March 2022
Overall Recruitment Status
Recruiting
Study Start Date
April 26, 2021 (Actual)
Primary Completion Date
April 30, 2022 (Anticipated)
Study Completion Date
June 28, 2022 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
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
Data Monitoring Committee
Yes
5. Study Description
Brief Summary
Evaluation of the colonic mucosa with a high definition colonoscope (EPKi7010 video processor).
The endoscopy images will be seen on a 27inch, flat-panel, high-definition LCD monitor (Radiance™ ultraSC-WU27-G1520 model) only by one expert endoscopist, randomly assigned.
The number, location, and polyps' features (Paris classification) will be recorded by the operator. If a polyp is detected, the endoscopist will remove the polyp endoscopically with a cold snare.
The same patient will be submitted to a second, the same session, computed aided real-time colonoscopy using the DISCOVERY, AI-assisted polyp detector. Colonoscopy will be performed by a same-level-of-expertise operator in comparison to the initial procedure. Any polyp or lesion detected with the AI system will be recorded and endoscopically removed and considered as a missed lesion from standard colonoscopy.
Detailed Description
Screening colonoscopy has decreased the incidence of colorectal carcinoma in the previous decades. However, there are reports of missed polyps and interval CRC following screening colonoscopy. Several factors may affect the ADR, PDR, and missed lesions rates, such as bowel preparation, percentage of mucosal surface evaluation, and the training levels of operators.
Artificial intelligence using deep-learning algorithms has been implemented in gastrointestinal endoscopy, mainly for the detection and diagnosis of GI tract lesions such as colonic polyps and adenomas. The implementation of automated polyp detection software during screening colonoscopy may prevent the missing of polyp and adenoma during screening colonoscopy. Therefore, improving the ADR and PDR during colonoscopies. All of this, with the aim of decrease the incidence of interval colorectal carcinoma (CRC), and CRC-related morbidity and mortality.
The Discovery Artificial Intelligence assisted polyp detector (Pentax Medical, Hoya Group) was recently launched for clinical practice. This AI software was trained with 120,000 files from approximately 300 clinical cases. The visual aided detection (bounding box locating a polyp on the monitor) will alert the endoscopist if a polyp/adenoma was missed during the standard, screening procedure.
To the best of our knowledge, this may be the first study evaluating the Discovery AI-assisted polyp detector on clinical practice in the western hemisphere. The investigators aim to evaluate the real-world effectiveness of AI-assisted colonoscopy in clinical practice. The investigators will also evaluate the role of endoscopists' levels of training in the ADR, PDR, and missed lesion rate.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Colorectal Neoplasms, Colon Polyp, Colon Adenoma
Keywords
colonoscopy, colorectal cancer
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Model Description
A non-blinded, non-randomized prospective diagnostic trial.
Two interventions:
Standard colonoscopy: 1 expert
AI-assisted colonoscopy: another expert
Masking
None (Open Label)
Allocation
N/A
Enrollment
209 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
Patients for CRC screening and diagnostic colonoscopy
Arm Type
Experimental
Arm Description
Consecutive patients >45 years of age submitted for diagnostic colonoscopy
Intervention Type
Diagnostic Test
Intervention Name(s)
Standard high-definition colonoscopy
Intervention Description
Evaluation of the colonic mucosa with a high definition colonoscope (EPKi7010 video processor).
The endoscopy images will be seen on a 27inch, flat panel, high-definition LCD monitor (Radiance™ ultraSC-WU27-G1520 model) only by one expert endoscopist, randomly assigned.
The number, location and polyps' features (Paris classification) will be recorded by the operator. If a polyp is detected, the endoscopist will remove the polyp endoscopically with a cold snare and forceps biopsy.
Intervention Type
Diagnostic Test
Intervention Name(s)
Colonoscopy with real-time AI assisted automated polyp detection
Intervention Description
The same patient will be submitted to a second, same session, computed aided real-time colonoscopy using the DISCOVERY, AI assisted polyp detector. Colonoscopy will be performed by a same-level-of-expertise operator in comparison to the initial procedure. Any polyp or lesion detected with the AI system will be recorded and endoscopically removed and considered as a missed lesion from standard colonoscopy.
Primary Outcome Measure Information:
Title
Adenoma detection rate of computer-aided after standard colonoscopy.
Description
Number of examinations with at least one adenoma detected during colonoscopy while using the AI-based model
Time Frame
30 days
Title
Polyp detection rate of computer-aided following standard colonoscopy.
Description
Number of examination with at least one polyp detected while using the AI-based model
Time Frame
30 days
Secondary Outcome Measure Information:
Title
Polyp miss rate of standard high-definition colonoscopy.
Description
Total number of missed polyps/ (total number of missed polyps + total number of polyps on initial examination)
Time Frame
30 days
Title
Adenoma miss rate of standard high-definition colonoscopy.
Description
Total number of missed adenomas/ (total number of missed adenomas + total number of adenomas on initial examination)
Time Frame
30 days
10. Eligibility
Sex
All
Minimum Age & Unit of Time
45 Years
Maximum Age & Unit of Time
80 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria:
Provided informed written consent
Age greater than 45 years of age
Adequate Bowel preparation
Exclusion Criteria:
History of inflammatory bowel disease, familial polyposis syndrome
History of colorectal carcinoma, colorectal surgery
History of uncontrolled coagulopathy
History of previously failed attempt colonoscopy
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Carlos Robles-Medranda, MD
Phone
+59342109180
Email
carlosoakm@yahoo.es
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Carlos Robles-Medranda, MD FASGE
Organizational Affiliation
Ecuadorian Institute of Digestive Diseases
Official's Role
Principal Investigator
Facility Information:
Facility Name
Ecuadorian Institute of Digestive Diseases
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 A Robles-Medranda, MD
Phone
+593989158865
Email
carlosoakm@yahoo.es
First Name & Middle Initial & Last Name & Degree
Carlos A Robles-Medranda, MD
First Name & Middle Initial & Last Name & Degree
Roberto A Oleas, MD
First Name & Middle Initial & Last Name & Degree
Martha G Arevalo-Mora, MD
First Name & Middle Initial & Last Name & Degree
Daniel Calle, MD
First Name & Middle Initial & Last Name & Degree
Miguel A Puga-Tejada, MD
12. IPD Sharing Statement
Plan to Share IPD
No
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
30926431
Citation
Vinsard DG, Mori Y, Misawa M, Kudo SE, Rastogi A, Bagci U, Rex DK, Wallace MB. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc. 2019 Jul;90(1):55-63. doi: 10.1016/j.gie.2019.03.019. Epub 2019 Mar 26.
Results Reference
background
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Computer-aided Detection During Screening Colonoscopy (Experts)
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