AI for Colorectal Polyp Detection in Endoscopy | NCT04339855

Studies
AI for Colorectal Polyp Detection in Endoscopy

NCT04339855

Sponsor

Johannes Gutenberg University Mainz

Status

Unknown status

Last Update Posted

Sep 7, 2020Actual

Enrollment

600Estimated

Phase

Not provided

Conditions

Focus of the Study is to Evaluate a New Developed Deep-learning Computer-aided Detection System in Combination With LCI for Colorectal Polyp Detection

Interventions

CAD with LCI for colorectal polyp detection

Countries

Germany

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

Sep 2020

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Active, not recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Feb 1, 2019Actual

Primary Completion Date

Aug 31, 2020Estimated

Completion Date

Sep 30, 2020Estimated

First Submitted Date

Apr 7, 2020

First Submission Date that Met QC Criteria

Apr 7, 2020

First Posted Date

Apr 9, 2020Actual

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

Sep 3, 2020

Last Update Posted Date

Sep 7, 2020Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Prof. Helmut Neumann, Director Interdisciplinary Endoscopy, Johannes Gutenberg University MainzPrincipal Investigator

Lead Sponsor

Johannes Gutenberg University MainzOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

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

Linked color imaging (LCI) has shown its effectiveness in multiple randomized controlled trials for enhanced colorectal polyp detection. Most recently, artificial intelligence (AI) with deep learning through convolutional neural networks has dramatically improved and is increasingly recognized as a promising new technique enhancing colorectal polyp detection. Study aim was to evaluate a new developed deep-learning computer-aided detection (CAD) system in combination with LCI for colorectal polyp detection.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

600Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
CAD with LCI for colorectal polyp detection	Other	Polyps within fully recorded endoscopy videos with LCI mode, covering the whole spectrum of adenomatous histology, are used to evaluate the efficacy of CAD with LCI for polyp detection.

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Colorectal polyp detection rate in comparison to traditional detection rate		2019-2020

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Full endoscopy withdrawal videos with LCI of patients ondergoing screening or surveillance endoscopy

Exclusion Criteria:

non adequate bowel preparation
no full length withdrawal in LCI mode

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Patients ondergoing screeining or surveillance endoscopy

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Helmut Neumann, Prof. Dr.	Head of Interdisciplinary Endoscopy	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
University Hospital Mainz		Mainz			Germany

IPD Sharing Statement Module

Plan to Share IPD

Undecided

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided