Focus of the Study is to Evaluate a New Developed Deep-learning Computer-aided Detection System in Combination With LCI for Colorectal Polyp Detection
Conditions
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.
Interventions
OTHERCAD with LCI for colorectal polyp detection
Polyps within fully recorded endoscopy videos with LCI mode, covering the whole spectrum of adenomatous histology, are used to evaluate the efficacy of CAD with LCI for polyp detection.
Sponsors
Johannes Gutenberg University Mainz
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Colorectal polyp detection rate in comparison to traditional detection rate | 2019-2020 |
Countries
Germany
Outcome results
None listed