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Efficacy of AI-Assisted Colonoscopy for Screening Colorectal Neoplasia (AI-COLOSCREEN)

NCT ID: NCT07307547

Last Updated: 2025-12-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

3342 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-12-31

Study Completion Date

2028-12-31

Brief Summary

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This study is a multi-center, randomized controlled trial designed to evaluate whether an artificial intelligence (AI) system can assist endoscopists to improve the detection rate of colorectal adenomas and cancers during colonoscopy compared to standard colonoscopy. Early screening and diagnosis are key to reducing the burden of colorectal cancer, but current colonoscopy has limitations, including the risk of missed lesions. This trial aims to determine if AI can enhance screening quality and diagnostic accuracy.

Detailed Description

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Colorectal cancer (CRC) screening is crucial for early detection and reducing mortality, yet current colonoscopy techniques face challenges such as variable adenoma detection rates (ADR) and the risk of missed diagnoses for subtle lesions. This study is a prospective, multi-center, parallel-group, randomized controlled trial aiming to validate the clinical value of an AI-assisted diagnostic system in improving screening quality. A total of 3342 participants will be randomized in a 1:1 ratio to undergo either AI-assisted colonoscopy (Experimental Group) or conventional high-definition colonoscopy (Control Group). The primary objective is to compare the ADR between the two groups. Secondary objectives include assessing the detection rate of advanced or specific types of polyps, the mean number of adenomas per procedure, and the impact of the AI system on both patient and physician satisfaction. The study will provide high-quality evidence for the standardized application of AI technology in CRC screening, with the ultimate goal of reducing the incidence and mortality of colorectal cancer.

Conditions

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Colorectal Neoplasms Colonic Polyp Adenoma Colorectal Cancer

Keywords

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Artificial Intelligence Colonoscopy Colorectal Cancer Screening Adenoma Detection Rate Deep Learning

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

SCREENING

Blinding Strategy

NONE

Study Groups

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Experimental: AI-Assisted Colonoscopy

Participants will undergo a high-definition colonoscopy procedure where a real-time artificial intelligence system analyzes the video feed to assist the endoscopist in identifying and highlighting suspicious lesions.

Group Type EXPERIMENTAL

AI-Assisted Colonoscopy

Intervention Type DEVICE

High-definition colonoscopy procedure with a real-time video analyzed artificial intelligence system.

Control: Conventional Colonoscopy

Participants will undergo a standard high-definition colonoscopy procedure performed by a qualified endoscopist without the assistance of the artificial intelligence system. The AI software will not be active during these procedures.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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AI-Assisted Colonoscopy

High-definition colonoscopy procedure with a real-time video analyzed artificial intelligence system.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

1. Age between 18 and 75 years, inclusive.
2. Scheduled for a screening, diagnostic, or surveillance colonoscopy.
3. Able to understand the study protocol and provide written informed consent.

Exclusion Criteria

1. Known contraindications to colonoscopy or biopsy.
2. Personal history of colorectal cancer, inflammatory bowel disease (IBD), or previous colorectal surgery.
3. Known or suspected colorectal polyposis syndrome (e.g., Familial Adenomatous Polyposis - FAP).
4. Patients with active colorectal bleeding, bowel obstruction, or toxic megacolon.
5. Women who are pregnant, planning to become pregnant, or are breastfeeding.
6. Participation in another interventional clinical trial within the 30 days prior to enrollment.
7. Any other condition that, in the investigator's judgment, would make the participant unsuitable for the study.
Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Zhejiang University

OTHER

Sponsor Role lead

Responsible Party

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Ding Ke-Feng

Clinical professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Kefeng Ding, M.D., Ph.D.

Role: STUDY_CHAIR

Second Affiliated Hospital, School of Medicine, Zhejiang University

Locations

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The Second Affiliated Hospital, Zhejiang University School of Medicine

Hangzhou, Zhejiang, China

Site Status

Countries

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China

Central Contacts

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Kefeng Ding, M.D., Ph.D.

Role: CONTACT

Phone: +86-13906504783

Email: [email protected]

Facility Contacts

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Kefeng Ding, M.D., Ph.D.

Role: primary

Other Identifiers

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2025-0756

Identifier Type: -

Identifier Source: org_study_id