Real-time Feedback of Red-out Within Colonoscopy Intubation
NCT ID: NCT07273890
Last Updated: 2025-12-10
Study Results
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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NOT_YET_RECRUITING
NA
576 participants
INTERVENTIONAL
2025-12-10
2028-04-20
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
PREVENTION
NONE
Study Groups
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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.
AI system
AI-system Performance Feedback in group B, group C, and group D.
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.
AI system
AI-system Performance Feedback in group B, group C, and group D.
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.
AI system
AI-system Performance Feedback in group B, group C, and group D.
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.
AI system
AI-system Performance Feedback in group B, group C, and group D.
Interventions
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AI system
AI-system Performance Feedback in group B, group C, and group D.
Eligibility Criteria
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Inclusion Criteria
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
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.
18 Years
70 Years
ALL
No
Sponsors
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The First Affiliated Hospital of Anhui Medical University
OTHER
Responsible Party
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Other Identifiers
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AHMU-Feedback of Red-out
Identifier Type: -
Identifier Source: org_study_id
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