Effectiveness of Artificial Intelligence-Assisted Colonoscopy for Adenoma Detection in Routine Clinical Practice : a Prospective Multicenter Randomized Trial

NCT07307547 | Not Yet Recruiting

• 1 other identifier: 2025-0756
• Study Type: interventional
• Target: 3,300
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Colorectal Neoplasms; Colonic Polyp; Adenoma; Colorectal Cancer

Keywords

Artificial Intelligence; Colonoscopy; Colorectal Cancer Screening; Adenoma Detection Rate; Deep Learning

Outcome Measures

Primary Outcomes (1)

• Adenoma Detection Rate (ADR)

  The proportion of participants with at least one histologically confirmed colorectal adenoma or adenocarcinoma. Detection and specimen collection occur during the colonoscopy, with final confirmation based on pathology reports.

  From the day of the procedure up to 14 days post-procedure.

Secondary Outcomes (7)

• Mean Adenomas Per Colonoscopy (APC)

  From the day of the procedure up to 14 days post-procedure.

• Advanced adenoma detection rate

  From the day of the procedure up to 14 days post-procedure.

• Serrated lesion detection rate

  From the day of the colonoscopy up to 14 days after the procedure.

• Patient Satisfaction Score

  Assessed within 1 hour after completion of the colonoscopy procedure.

• Proximal adenoma detection rate

  From the day of the colonoscopy up to 14 days after the procedure.

Study Arms (2)

Experimental: AI-Assisted Colonoscopy

EXPERIMENTAL

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.

Device: AI-Assisted Colonoscopy

Control: Conventional Colonoscopy

NO INTERVENTION

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.

Interventions

AI-Assisted Colonoscopy DEVICE

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

Experimental: AI-Assisted Colonoscopy

Eligibility Criteria

• Age: 40 Years - 74 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age 40 to 74 years, inclusive; men and women.
• Scheduled for elective screening, diagnostic, or surveillance colonoscopy.
• In generally good condition, mentally competent, and willing to provide written informed consent.

You may not qualify if:

• Known contraindication to colonoscopy or to biopsy.
• History of colon cancer or previous colonic surgery.
• Bowel obstruction; confirmed or highly suspected colorectal cancer; inflammatory bowel disease; or active colorectal bleeding.
• Colorectal polyposis, including a history of familial adenomatous polyposis.
• Pregnancy or breastfeeding.
• Participation in another drug or medical device clinical trial within 1 month before enrollment.
• Any other condition that, in the investigator's judgment, makes the participant unsuitable for this trial.
• Withdrawal Criteria:
• Withdrawal of consent by the participant.
• Inadequate bowel preparation, defined as a Boston Bowel Preparation Scale (BBPS) total score below 6 or any segment score below 2.
• Failure to reach the cecum.
• Newly diagnosed active colitis during the procedure.

Sponsors & Collaborators

• Zhejiang University: lead

Study Sites (1)

The Second Affiliated Hospital, Zhejiang University School of Medicine

Hangzhou, Zhejiang, 310009, China

Location

MeSH Terms

Conditions

Colorectal Neoplasms; Colonic Polyps; Adenoma

Condition Hierarchy (Ancestors)

Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases; Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Study Officials

• Kefeng Ding, M.D., Ph.D.

  Second Affiliated Hospital, Zhejiang University, School of Medicine

  STUDY CHAIR

Central Study Contacts

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

CONTACT

+86-13906504783; dingkefeng@zju.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: Participants are blinded to group assignment. Endoscopists performing the procedures cannot be blinded, which is an inherent limitation of computer-aided detection trials. Histopathological assessment, Boston Bowel Preparation Scale scoring by a second observer, outcome adjudication, and statistical analysis are performed by blinded personnel; the statistician remains blinded until database lock.
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical professor

Study Record Dates

• First Submitted: December 14, 2025
• First Posted: December 29, 2025
• Study Start: September 1, 2026
• Primary Completion (Estimated): December 1, 2028
• Study Completion (Estimated): December 1, 2028
• Last Updated: September 16, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)