Clinical trial NCT05611151: Real-time Computer-Aided Detection of Colonic Adenomas With NEC WISE VISION® Endoscopy
Official title: Real-time Computer-Aided Detection of Colonic Adenomas With NEC WISE VISION® Endoscopy: Prospective, Randomized Clinical Performance Evaluation
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Colorectal Cancer
Adenoma Colon
Polyp of Colon
Keywords: Screening
Artificial Intelligence
Lead sponsor: NEC Corporation
Sponsor class: INDUSTRY
Collaborators: name: Meditrial Europe Ltd.
name: Meditrial USA Inc.
First posted: 2022-11-09
Enrollment: 830
Sex: ALL
Minimum age (years): 45
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Parallel trial design
Primary purpose: DIAGNOSTIC

Brief summary: This is a prospective, multicenter, randomized study to evaluate the clinical performance of a novel CADe device, WISE VISION® Endoscopy System, in patients undergoing high-definition white light (HDWL) colonoscopy for screening or surveillance of colorectal Cancer (CRC).

Eligible subjects who meet the study inclusion/exclusion criteria will be randomized in a 1:1 ratio to undergo colonoscopy :

* Experimental: CADe colonoscopy procedure with WISE VISION® Endoscopy (CADe Group)
* Control: Standard Colonoscopy without CADe (Standard Colonoscopy Group)

Detailed description: This study aims to evaluate whether NEC WISE VISION® Endoscopy enhances the ability to detect mucosal lesions when compared with the current standard-of-care procedure (high-definition white light \[HDWL\] colonoscopy). The CADe device, Wise VISION® Endoscopy, contains an artificial intelligence/machine learning (AI/ML) advanced algorithm to aid the endoscopists in detection of colonic lesions.

This study plans to enrol 830 subjects aged ≥ 45 years and \< 75 years, who are scheduled for screening or surveillance colonoscopy.

After review of the inclusion and exclusion criteria, eligible subjects will be randomized in a 1:1 ratio to receive either computer-aided colonoscopy (CADe Group) or standard colonoscopy without CADe (Standard Colonoscopy Group).

All adenomas, that are identified during the colonoscopies, will be removed and biopsied as per standard clinical practice to assess adenoma miss rate, polyp miss rate, adenomas per colonoscopy and other endpoints.

Eligibility criteria: Inclusion Criteria:

* Patient is presenting to the endoscopy unit for colon cancer screening or surveillance colonoscopy.
* Signed Informed Consent

Exclusion Criteria:

1. Contraindications to colonoscopy
2. Colonoscopy within the previous three (3) years
3. High-risk indications for colonoscopy
4. Antithrombotic therapy that cannot be stopped, precluding polyp resection
5. Inflammatory bowel disease
6. Referred for endoscopic mucosal resection (EMR)
7. Familial adenomatous polyposis syndrome or Serrated Polyposis Syndrome
8. Pregnant or planning a pregnancy during the study period

Primary outcomes:
measure: Adenomas per Colonoscopy (APC)
timeFrame: during the procedure/surgery
Secondary outcomes:
Other outcomes:
Locations:
city: Kansas City
country: United States
facility: Kansas City VA Hospital
state: Missouri
city: Bad Salzuflen
country: Germany
facility: GastroZentrum Lippe
city: Castellanza
country: Italy
facility: Humanitas Mater Domini
city: Milan
country: Italy
facility: Humanitas Research Hospital
city: Portsmouth
country: United Kingdom
facility: Portsmouth Hospitals University NHS Trust, Cosham
Study officials:
affiliation: NEC Corporation
name: Hitoshi Ikeda
role: STUDY_DIRECTOR
References:
citation: Yamada M, Saito Y, Imaoka H, Saiko M, Yamada S, Kondo H, Takamaru H, Sakamoto T, Sese J, Kuchiba A, Shibata T, Hamamoto R. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019 Oct 8;9(1):14465. doi: 10.1038/s41598-019-50567-5.