Effect of Two Colonoscopy AI Systems for Colon Polyp Detection
Overview
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. The investigators aim to identify the effect of two CADe systems according to the system performance on false positive rate
Full Title of Study: “Effect of Two Colonoscopy AI Systems for Colon Polyp Detection According to the False Positive Rates of the Systems: A Single-center Prospective Study”
Study Type
- Study Type: Interventional
- Study Design
- Allocation: Non-Randomized
- Intervention Model: Parallel Assignment
- Primary Purpose: Diagnostic
- Masking: Single (Outcomes Assessor)
- Study Primary Completion Date: October 31, 2022
Detailed Description
Artificial intelligence technology based on deep learning is being applied in various medical fields, and research is being actively conducted to develop computer-aided detection (CADe) systems for colonoscopies to overcome the limitation of the variance of human skills. These well-trained CADe systems demonstrated high performance for neoplastic polyp detection and reported a 44% increase in adenoma detection rate (ADR) for endoscopists. However, the level of performance in the CADe system is not clear for expert endoscopists to be useful for ADR increase. Furthermore, false positives(FPs) of the CADe system may negatively influence ADR during a screening colonoscopy. Accordingly, the investigators sought to identify the effect of the colonoscopy CADe system according to FP performance in endoscopists with various levels. The investigators hypothesized that the CADe system with low FPs would be useful to prevent the decrease in ADR in case of a high endoscopy workload according to the performance of CADe systems.
Interventions
- Device: Assist by artificial intelligence system for colon polyp detection
- Assist by artificial intelligence system for colon polyp detection
Arms, Groups and Cohorts
- Experimental: CADe group
- Endoscopists perform colonoscopy with CADe system
- No Intervention: Control
- Endoscopists perform colonoscopy without CADe system
Clinical Trial Outcome Measures
Primary Measures
- Adenoma detection rate
- Time Frame: 12 months
- proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician.
- Sessile serrated lesion detection rate
- Time Frame: 12 months
- proportion of colonoscopies with at least one sessile serrated lesion detected overall and as detected by the physician.
Secondary Measures
- polyp detection rate
- Time Frame: 12 months
- proportion of colonoscopies with at least one polyp detected overall and as detected by the physician.
Participating in This Clinical Trial
Inclusion Criteria
patient for screening or surveillance colonoscopy patients agreed with participating in the study Exclusion Criteria:
patients who do not agree with participating in the study patients with a history of colon resection patients with a history of inflammatory bowel resection patients with poor bowel preparation
Gender Eligibility: All
Minimum Age: 45 Years
Maximum Age: 100 Years
Are Healthy Volunteers Accepted: Accepts Healthy Volunteers
Investigator Details
- Lead Sponsor
- Seoul National University Hospital
- Collaborator
- Seoul National University
- Provider of Information About this Clinical Study
- Sponsor
- Overall Official(s)
- Jung Ho Bae, MD, Principal Investigator, Seoul National University Hospital
References
Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26.
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