Deep-Learning for Automatic Polyp Detection During Colonoscopy

Screening Colonoscopy Clinical Trial

Official title:

Deep-Learning for Automatic Polyp Detection During Colonoscopy

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03637712
• Other study ID #: 18-00746
• Status: Completed
• Phase: N/A
• Start date: September 1, 2018
• Est. completion date: July 7, 2019

Study information

• Verified date: May 2020
• Source: NYU Langone Health
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 5
• Est. completion date: July 7, 2019
• Est. primary completion date: July 7, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 99 Years

Eligibility

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.

Related Conditions & MeSH terms

• Screening Colonoscopy

Intervention

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

Locations

Country	Name	City	State
United States	NYU Langone Health	New York	New York

Sponsors (1)

Lead Sponsor	Collaborator
NYU Langone Health

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Adenoma Detection Rate	the proportion of colonoscopic examinations performed that detect one or more polyp	1 Day