Deep-Learning for Automatic Polyp Detection During Colonoscopy
Status
Completed
Conditions
Screening Colonoscopy
Treatments
Device: Computer Algorithm
Details and patient eligibility
About
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.
Enrollment
5 patients
Sex
All
Ages
18 to 99 years old
Volunteers
No Healthy Volunteers
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.
Trial design
Primary purpose
Diagnostic
Allocation
N/A
Interventional model
Single Group Assignment
Masking
None (Open label)
5 participants in 1 patient group
Screening Colonoscopy
Experimental group
Description:
Patients undergoing standard screening or surveillance colonoscopy will be included
Treatment:
Device: Computer Algorithm
Trial contacts and locations
1
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Data sourced from clinicaltrials.gov
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