Real-time Feedback of Red-out Within Colonoscopy Intubation

November 27, 2025 updated by: The First Affiliated Hospital of Anhui Medical University

Prospective, Multicenter, Controlled Study on the Impact of Real-time Feedback on Red-out

This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D.

The research will primarily focus on the AI-based analysis of colonoscopic images to calculate the following metrics: caecal intubation time, red-out percentage, and the AI-based red-out avoiding score. Based on the study's implementation protocol, a decision will be made regarding whether to provide real-time feedback. Additionally, the presence of any complications will be assessed both during and after the colonoscopy procedure.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

576

Phase

  • Not Applicable

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  1. Study Participants (Patients):

    Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.

  2. Colonoscopists:

Expert-level colonoscopists (having performed a total of >1000 colonoscopy procedures).

Right-handed.

Exclusion Criteria:

  1. Study Participants (Patients):

    Individuals undergoing the following procedures:

    cases with a history of colorectal surgery; cases with a history of chemotherapy, raditherapy; cases with a history of abdominal, and/or pelvic surgery; cases with a history of difficult colonoscopies; cases with colorectal tumours and obstructive lesions; cases with colorectal diverticula; cases with ulcerative colitis or Crohn's disease; cases with ischemic bowel disease; cases with colorectal polyposis; cases with melanosis coli; cases undergoing sigmoidoscopy; cases with poor intestional cleanliness (segment Boston bowel preparation scale (BBPS) of < 2 points, total BBPS of < 6 points); cases undergoing therapy procedures such as biopsy or CSP during the intubation phase; cases with transparent cap assisted colonoscopy; cases with water-assisted colonoscopy; cases with air insufflation level of M or L; cases failed caecal intubation within 15 min; cases with colonoscope stiffness level > 0; obese cases or underweight cases; and cases refusing participation.

    Individuals who decline to provide informed consent.

  2. Colonoscopists:

Those who have performed fewer than 300 complete colonoscopies in any calendar year within the past three years.

Those who decline to participate in the study.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Sham Comparator: Group A
During colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time; however, these results are not provided as feedback to the operating colonoscopist.
Other: AI system
AI-system Performance Feedback in group B, group C, and group D.
Experimental: Group B
During colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time, with only the caecal intubation time being provided as feedback to the operator, while the red-out percentage and AI-based red-out avoiding score are withheld.
Other: AI system
AI-system Performance Feedback in group B, group C, and group D.
Experimental: Group C
During colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time, with only the red-out percentage being provided as feedback to the operator, while the caecal intubation time and AI-based red-out avoiding score are withheld.
Other: AI system
AI-system Performance Feedback in group B, group C, and group D.
Experimental: Group D
During colonoscopy intubation, AI system is used to calculate and analyze the "caecal intubation time" "red-out percentage," and "AI-based red-out avoiding score" in real-time, with all three results provided as feedback to the operating colonoscopist.
Other: AI system
AI-system Performance Feedback in group B, group C, and group D.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Red-out percentage
Time Frame: Stage 3 (projected to begin in 3-6 months)
The impact of real-time feedback on red-out percentage.
Stage 3 (projected to begin in 3-6 months)
Caecal intubation time
Time Frame: Stage 3 (projected to begin in 3-6 months)
The impact of real-time feedback on caecal intubation time.
Stage 3 (projected to begin in 3-6 months)
AI-based red-out avoiding score
Time Frame: Stage 3 (projected to begin in 3-6 months)
The impact of real-time feedback on AI-based red-out avoiding score.
Stage 3 (projected to begin in 3-6 months)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Complications
Time Frame: Stage 3 (projected to begin in 3-6 months)
During and after the colonoscopy, assess for any signs of complications
Stage 3 (projected to begin in 3-6 months)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The First Affiliated Hospital of Anhui Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

December 10, 2025

Primary Completion (Estimated)

August 31, 2027

Study Completion (Estimated)

April 20, 2028

Study Registration Dates

First Submitted

November 27, 2025

First Submitted That Met QC Criteria

November 27, 2025

First Posted (Actual)

December 10, 2025

Study Record Updates

Last Update Posted (Actual)

December 10, 2025

Last Update Submitted That Met QC Criteria

November 27, 2025

Last Verified

October 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AHMU-Feedback of Red-out

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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