Computer-aided Detection During Screening Colonoscopy (Experts)
Study Details
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Patients for CRC screening and diagnostic colonoscopy Consecutive patients >45 years of age submitted for diagnostic colonoscopy |
Diagnostic Test: Standard high-definition colonoscopy
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.
Diagnostic Test: Colonoscopy with real-time AI assisted automated polyp detection
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.
|
Outcome Measures
Primary Outcome Measures
- Adenoma detection rate of computer-aided after standard colonoscopy. [30 days]
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. [30 days]
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. [30 days]
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. [30 days]
Total number of missed adenomas/ (total number of missed adenomas + total number of adenomas on initial examination)
Eligibility Criteria
Criteria
Inclusion Criteria:
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Provided informed written consent
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Age greater than 45 years of age
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Adequate Bowel preparation
Exclusion Criteria:
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History of inflammatory bowel disease, familial polyposis syndrome
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History of colorectal carcinoma, colorectal surgery
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History of uncontrolled coagulopathy
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History of previously failed attempt colonoscopy
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Ecuadorian Institute of Digestive Diseases | Guayaquil | Guayas | Ecuador | 090505 |
Sponsors and Collaborators
- Instituto Ecuatoriano de Enfermedades Digestivas
Investigators
- Principal Investigator: Carlos Robles-Medranda, MD FASGE, Ecuadorian Institute of Digestive Diseases
Study Documents (Full-Text)
None provided.More Information
Publications
- 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. Review.
- 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.
- IECED-042621