Deep-Learning for Automatic Polyp Detection During Colonoscopy

Not Applicable

Completed

• Conditions: Screening Colonoscopy
• Interventions: Device: Computer Algorithm

• 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

Study Timeline

• Study First Submitted: 2018-08-16
• Study First Submitted QC: 2018-08-16
• Study First Posted: 2018-08-20
• Study Start: 2018-09-01
• Primary Completion: 2019-07-07
• Study Completion: 2019-07-07
• Status Verified: 2020-05
• Last Update Submitted: 2020-05-14
• Last Update Posted: 2020-05-15

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

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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.

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Screening Colonoscopy	Computer Algorithm	Patients undergoing standard screening or surveillance colonoscopy will be included

Primary Outcome Measures

Name	Time	Method
Adenoma Detection Rate	1 Day	the proportion of colonoscopic examinations performed that detect one or more polyp

Trial Locations

Locations (1)

NYU Langone Health; 🇺🇸 New York, New York, United States