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 Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
NONE
Enrollment
576
AI-system Performance Feedback in group B, group C, and group D.
Red-out percentage
The impact of real-time feedback on red-out percentage.
Time frame: Stage 3 (projected to begin in 3-6 months)
Caecal intubation time
The impact of real-time feedback on caecal intubation time.
Time frame: 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.
Time frame: Stage 3 (projected to begin in 3-6 months)
Complications
During and after the colonoscopy, assess for any signs of complications
Time frame: Stage 3 (projected to begin in 3-6 months)
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