Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.
Shunsuke Kamba, Naoto Tamai, Iduru Saitoh...
https://pubmed.ncbi.nlm.nih.gov/34218329Actively Recruiting
Led by Department of Medical Services Ministry of Public Health of Thailand · Updated on 2024-12-13
240
Participants Needed
1
Research Sites
2 weeks
Total Duration
Researchers are assessing how well artificial intelligence (AI) can help detect polyps on the right side of the colon during colonoscopy. The study focuses on comparing the effectiveness of AI-assisted detection with traditional methods when performed by endoscopists with different levels of experience. This trial aims to improve polyp detection rates, especially since polyps can be missed on the right side of the colon. Participants undergo colonoscopy with double insertion of the right-sided colon under white light. Four groups are compared: experienced endoscopists using AI, beginner endoscopists using AI, experienced endoscopists without AI (control), and beginner endoscopists without AI (control). The AI system, called CADe, assists in polyp detection during the second endoscopic withdrawal. During the study, researchers measure how often polyps are detected during the procedure and identify the types of polyps found. Participants will be monitored throughout the procedure to assess outcomes. The study includes adults aged 40 to 80 years and involves random assignment to one of the four groups to compare the role of AI and experience in detecting right-sided colonic polyps.
CONDITIONS
Artificial Intelligence in the Detection of Right Sided Colonic Polyp in Different Operator Experience
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Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Duration - 1 day
Participants undergo colonoscopy with double insertion of the right sided colon under either conventional white light or AI assistance to detect polyps.
1 procedure visit (in-person)
Total: 1 location
1
Rajavithi Hospital
Bangkok, Bangkok, Thailand
Actively Recruiting
Study Type
INTERVENTIONAL
Masking
SINGLE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
SCREENING
Number of Arms
4
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Shunsuke Kamba, Naoto Tamai, Iduru Saitoh...
https://pubmed.ncbi.nlm.nih.gov/34218329