AI for Colorectal Polyp Detection in Endoscopy

September 3, 2020 updated by: Prof. Helmut Neumann, Johannes Gutenberg University Mainz

Artificial Intelligence Combined With LCI for Colorectal Polyp Detection

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.

Study Overview

Study Type

Observational

Enrollment (Anticipated)

600

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

      • Mainz, Germany
        • University Hospital Mainz

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients ondergoing screeining or surveillance endoscopy

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Principal Investigator: Helmut Neumann, Prof. Dr., Head of Interdisciplinary Endoscopy

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 1, 2019

Primary Completion (Anticipated)

August 31, 2020

Study Completion (Anticipated)

September 30, 2020

Study Registration Dates

First Submitted

April 7, 2020

First Submitted That Met QC Criteria

April 7, 2020

First Posted (Actual)

April 9, 2020

Study Record Updates

Last Update Posted (Actual)

September 7, 2020

Last Update Submitted That Met QC Criteria

September 3, 2020

Last Verified

September 1, 2020

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • HN_01KR7Zt

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on CAD with LCI for colorectal polyp detection

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