Deep-Learning for Automatic Polyp Detection During Colono... | NCT03637712

Studies
Deep-Learning for Automatic Polyp Detection During Colonoscopy

NCT03637712

Sponsor

NYU Langone Health

Status

Completed

Last Update Posted

May 15, 2020Actual

Enrollment

5Actual

Phase

Not Applicable

Conditions

Screening Colonoscopy

Interventions

Computer Algorithm

Countries

United States

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

May 2020

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Sep 1, 2018Actual

Primary Completion Date

Jul 7, 2019Actual

Completion Date

Jul 7, 2019Actual

First Submitted Date

Aug 16, 2018

First Submission Date that Met QC Criteria

Aug 16, 2018

First Posted Date

Aug 20, 2018Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

May 14, 2020

Last Update Posted Date

May 15, 2020Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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 provided

Design Module

Study Type

Interventional

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Not provided

Target Follow-Up Duration

Not provided

Phases

Not Applicable

Interventional Study Design

Allocation

Biospecimen

No data available

No data is available for this block.

Enrollment

5Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Screening Colonoscopy	Experimental	Patients undergoing standard screening or surveillance colonoscopy will be included	Device: Computer Algorithm

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Computer Algorithm	Device	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

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Adenoma Detection Rate	the proportion of colonoscopic examinations performed that detect one or more polyp	1 Day

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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.

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

99 Years

Standard Ages

AdultOlder Adult

Study Population

Not provided

Sampling Method

Not provided

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Seth Gross, MD	NYU Langone Health	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
NYU Langone Health		New York	New York	10016	United States