Real-time Feedback of Red-out Within Colonoscopy Intubation

NCT07273890 | Not Yet Recruiting DMC

• 1 other identifier: AHMU-Feedback of Red-out
• Study Type: interventional
• Target: 576
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Artificial Intelligence; Colonoscopy; Real-time Feedback

Keywords

Colonoscopy; Intubation; Real-time feedback; Red-out

Outcome Measures

Primary Outcomes (3)

• Red-out percentage

  The impact of real-time feedback on red-out percentage.

  Stage 3 (projected to begin in 3-6 months)

• Caecal intubation time

  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

  The impact of real-time feedback on AI-based red-out avoiding score.

  Stage 3 (projected to begin in 3-6 months)

Secondary Outcomes (1)

• Complications

  Stage 3 (projected to begin in 3-6 months)

Study Arms (4)

Group A

SHAM COMPARATOR

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

Group B

EXPERIMENTAL

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

Group C

EXPERIMENTAL

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

Group D

EXPERIMENTAL

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

Interventions

AI system OTHER

AI-system Performance Feedback in group B, group C, and group D.

Group A; Group B; Group C; Group D

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Study Participants (Patients):
• Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.
• Colonoscopists:
• Expert-level colonoscopists (having performed a total of \>1000 colonoscopy procedures).
• Right-handed.

You may not qualify if:

• 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.
• 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.

Sponsors & Collaborators

• The First Affiliated Hospital of Anhui Medical University: lead

MeSH Terms

Interventions

Intelligent Systems

Intervention Hierarchy (Ancestors)

Artificial Intelligence; Algorithms; Mathematical Concepts

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 27, 2025
• First Posted: December 10, 2025
• Study Start: December 10, 2025
• Primary Completion (Estimated): August 31, 2027
• Study Completion (Estimated): April 20, 2028
• Last Updated: December 10, 2025

Record last verified: 2025-10