Real-time Feedback of Red-out Within Colonoscopy Intubation

The First Affiliated Hospital of Anhui Medical University

Status: Not yet enrolling

Conditions: Real-time Feedback; Artificial Intelligence; Colonoscopy

Treatments: Other: AI system

Study type: Interventional

Funder types: Other

Identifiers

NCT07273890

AHMU-Feedback of Red-out

Details and patient eligibility

About

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.

Enrollment: 576 estimated patients

Sex: All

Ages: 18 to 70 years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Prevention

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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576 participants in 4 patient groups

Group A

Sham Comparator group

Description:

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.

Treatment:

Other: AI system

Group B

Experimental group

Description:

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.

Treatment:

Other: AI system

Group C

Experimental group

Description:

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.

Treatment:

Other: AI system

Group D

Experimental group

Description:

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.

Treatment:

Other: AI system