About this trial
In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control.
This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control.
This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
age > 50 years old;
required diagnostic colonoscopy, screening colonoscopy, or follow-up colonoscopy;
voluntarily sign informed consent;
Commitment to abide by the study procedures and cooperate with the implementation of the whole process of the study.
Disqualifiers
have participated in other clinical trials, signed informed consent and are in the follow-up period of other clinical trials;
known polyposis syndrome patients;
patients with known IBD;
patients considered by the investigators to be unsuitable or unable to undergo complete digestive endoscopy and related examinations;
Trial population
patients required diagnostic colonoscopy, screening colonoscopy, or follow-up colonoscopy
Trial design
Other
Prospective
Treatments tested in this trial
ANDOANGEL
DevicePolyps were identified by endoscopists assisted by an AI system: rectangular box marks; Monitoring of ileocecal position: whether blindness was reached was displayed in the lower left corner of the interface. Mirror entry and exit time monitoring: the operation time is displayed in the upper left corner of the interface. Colonoscopy withdrawal speed monitoring: the relative withdrawal speed was displayed on the left side of the interface.
Treatment groups
Trial outcomes
Primary outcomes
Adenoma detection rate
The numerator is the number of patients who had at least one adenoma on colonoscopy, and the denominator is the total number of patients who underwent colonoscopy
Secondary outcomes
Polyp detection rate
The numerator is the number of patients who had at least one polyp on colonoscopy, and the denominator is the total number of patients who underwent colonoscopy
Detection rate of serrated adenoma
The numerator is the number of patients who had at least one serrated adenoma on colonoscopy, and the denominator is the total number of patients who underwent colonoscopy
Average number of polyps per colonoscopy
The numerator is the total number of polyps detected by colonoscopy, and the denominator is the total number of colonoscopies
Colonoscopy time
The pure negative time of the whole examination did not include the time of endoscopy entry and the time of biopsy
Sponsors and contacts
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