Real-Time Artificial Intelligence Assissted Colonoscopy to Identify and Classify Polyps
- Conditions
- Artificial IntelligenceColonoscopyQuality Control
- Interventions
- Diagnostic Test: DeFrameDiagnostic Test: conventional colonoscopyDiagnostic Test: Classified DeFrame
- Registration Number
- NCT05718193
- Lead Sponsor
- Xiangya Hospital of Central South University
- Brief Summary
To investigate the degree of the real-time detection and classification system for increasing the adenoma detection rate during colonoscopy.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 2868
Aged 18 to 85 years old. Colonoscopies for primary CRC screening of the subjects are required.
History of CRC,inflammatory bowel disease, previous colonic resection, antithrombotic therapy precluding polyp resection.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description The DeFrame Group DeFrame Subjects in the DeFrame group were treated with a real-time computer-aided polyp detection system named DeFrame during colonoscopy. The Control Group conventional colonoscopy Subjects in the control group underwent standard colonoscopy. The Classified DeFrame Group Classified DeFrame Subjects in the Classified DeFrame group were treated with a real-time computer-aided polyp detection and classification system named Classified DeFrame during colonoscopy.
- Primary Outcome Measures
Name Time Method adenoma detection rate up to 9 months Percentage of patients who have 1 or more histologically confirmed adenoma resected divided by the total number of colonoscopies.
adenomas per colonoscopy up to 9 months Total number of histologically confirmed adenomas resected divided by the total number of colonoscopies.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (2)
Xiangya Hospital Central South University
🇨🇳Changsha, Hunan, China
Loudi Central Hospital
🇨🇳Loudi, Hunan, China