Clinical trial NCT04915833: 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
Overall status: UNKNOWN
Phase: NA
Conditions: Colorectal Neoplasms
Colon Polyp
Colon Adenoma
Keywords: colonoscopy
colorectal cancer
Lead sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Sponsor class: OTHER
First posted: 2021-06-07
Enrollment: 209
Sex: ALL
Minimum age (years): 45
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: A non-blinded, non-randomized prospective diagnostic trial.

Two interventions:

* Standard colonoscopy: 1 expert
* AI-assisted colonoscopy: another expert
Primary purpose: DIAGNOSTIC

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.

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

Primary outcomes:
measure: Adenoma detection rate of computer-aided after standard colonoscopy.
timeFrame: 30 days
measure: Polyp detection rate of computer-aided following standard colonoscopy.
timeFrame: 30 days
Secondary outcomes:
measure: Polyp miss rate of standard high-definition colonoscopy.
timeFrame: 30 days
measure: Adenoma miss rate of standard high-definition colonoscopy.
timeFrame: 30 days
Other outcomes:
Locations:
city: Guayaquil
country: Ecuador
facility: Ecuadorian Institute of Digestive Diseases
state: Guayas
Study officials:
affiliation: Ecuadorian Institute of Digestive Diseases
name: Carlos Robles-Medranda, MD FASGE
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.