Trials / Completed
CompletedNCT03637712
Deep-Learning for Automatic Polyp Detection During Colonoscopy
- Status
- Completed
- Phase
- N/A
- Study type
- Interventional
- Enrollment
- 5 (actual)
- Sponsor
- NYU Langone Health · Academic / Other
- Sex
- All
- Age
- 18 Years – 99 Years
- Healthy volunteers
- Not accepted
Summary
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| DEVICE | Computer Algorithm | This device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps |
Timeline
- Start date
- 2018-09-01
- Primary completion
- 2019-07-07
- Completion
- 2019-07-07
- First posted
- 2018-08-20
- Last updated
- 2020-05-15
Locations
1 site across 1 country: United States
Source: ClinicalTrials.gov record NCT03637712. Inclusion in this directory is not an endorsement.