Deep-Learning for Automatic Polyp Detection During Colonoscopy
Not Applicable
Completed
- Conditions
- Screening Colonoscopy
- Registration Number
- NCT03637712
- Lead Sponsor
- NYU Langone Health
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 5
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.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Primary Outcome Measures
Name Time Method Adenoma Detection Rate 1 Day the proportion of colonoscopic examinations performed that detect one or more polyp
- Secondary Outcome Measures
Name Time Method
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What molecular mechanisms underlie deep-learning algorithms in enhancing adenoma detection during colonoscopy?
How does the computer algorithm in NCT03637712 compare to standard colonoscopy in terms of adenoma detection rates and diagnostic accuracy?
Are there specific biomarkers or imaging features that correlate with improved polyp detection using AI in screening colonoscopies?
What are the potential adverse events associated with integrating AI software into real-time colonoscopy procedures, and how are they managed?
What other AI-based or device interventions are being developed for colorectal cancer screening alongside NYU Langone Health's trial?
Trial Locations
- Locations (1)
NYU Langone Health
🇺🇸New York, New York, United States
NYU Langone Health🇺🇸New York, New York, United States