AI for Colorectal Polyp Detection in Endoscopy
- Conditions
- Focus of the Study is to Evaluate a New Developed Deep-learning Computer-aided Detection System in Combination With LCI for Colorectal Polyp Detection
- Registration Number
- NCT04339855
- Lead Sponsor
- Johannes Gutenberg University Mainz
- Brief Summary
Linked color imaging (LCI) has shown its effectiveness in multiple randomized controlled trials for enhanced colorectal polyp detection. Most recently, artificial intelligence (AI) with deep learning through convolutional neural networks has dramatically improved and is increasingly recognized as a promising new technique enhancing colorectal polyp detection. Study aim was to evaluate a new developed deep-learning computer-aided detection (CAD) system in combination with LCI for colorectal polyp detection.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 600
Inclusion Criteria
- Full endoscopy withdrawal videos with LCI of patients ondergoing screening or surveillance endoscopy
Exclusion Criteria
- non adequate bowel preparation
- no full length withdrawal in LCI mode
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Colorectal polyp detection rate in comparison to traditional detection rate 2019-2020
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
University Hospital Mainz
🇩🇪Mainz, Germany