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Deep-Learning for Automatic Polyp Detection During Colonoscopy

NCT03637712 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 5

Last updated 2020-05-15

No results posted yet for this study

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.

Conditions

  • Screening Colonoscopy

Interventions

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

Sponsors & Collaborators

Principal Investigators

  • Seth Gross, MD · NYU Langone Health

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
99 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-09-01
Primary Completion
2019-07-07
Completion
2019-07-07

Countries

  • United States

Study Locations

More Related Trials

Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03637712 on ClinicalTrials.gov