Deep-Learning for Automatic Polyp Detection During Colonoscopy
| Status: | Recruiting |
|---|---|
| Healthy: | No |
| Age Range: | 18 - 99 |
| Updated: | 12/20/2018 |
| Start Date: | September 1, 2018 |
| End Date: | September 2019 |
| Contact: | Christopher Hawrluk |
| Email: | Christopher.Hawrluk@nyumc.org |
| Phone: | 1 646 501 2322 |
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.
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.
Inclusion Criteria:
- Patients presenting for routine colonoscopy for screening and/or surveillance
purposes.
- Ability to provide written, informed consent and understand the responsibilities of
trial participation
Exclusion Criteria:
- People with diminished cognitive capacity.
- The subject is pregnant or planning a pregnancy during the study period.
- Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
- Patients with incomplete colonoscopies (those where endoscopists did not successfully
intubate the cecum due to technical difficulties or poor bowel preparation)
- Patients that have standard contraindications to colonoscopy in general (e.g.
documented acute diverticulitis, fulminant colitis and known or suspected
perforation).
- Patients with inflammatory bowel disease
- Patients with any polypoid/ulcerated lesion > 20mm concerning for invasive cancer on
endoscopy.
We found this trial at
1
site
462 1st Avenue
New York, New York 10010
New York, New York 10010
Principal Investigator: Seth Gross, MD
Phone: 646-501-2322
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