Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence

December 21, 2018 updated by: Yanqing Li, Shandong University
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastrointestinal mucosa during ongoing endoscopy examination. It can predict the classification of Colorectal Polyps accurately. However this requires much experience, which limits the application of pCLE. The investigators designed a computer program using deep neural networks to differentiate hyperplastic from neoplastic polyps automatically in pCLE examination.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Anticipated)

200

Phase

  • Not Applicable

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

  • China
    • Shandong
      • Jinan, Shandong, China, 250001
        • Recruiting
        • Endoscopic unit of Qilu Hospital Shandong University
        • Contact:
          • Yanqing Li, PhD,MD

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 to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Description

Inclusion Criteria:

aged between 18 and 80; agree to give written informed consent.

Exclusion Criteria:

Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium; Inability to provide informed consent

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

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Triple

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI visible group
Other: AI presentation
Automatic diagnosis information of AI is visible to endoscopist
No Intervention: AI invisible group

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks
Time Frame: 4 months
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing Colorectal Polyps on real-time pCLE examination.
4 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
Time Frame: 3 month
The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing Colorectal Polyps on real-time pCLE examination) between Artificial Intelligence and endoscopists.
3 month

Collaborators and Investigators

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

Sponsor

Shandong University

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)

May 1, 2018

Primary Completion (Anticipated)

January 30, 2019

Study Completion (Anticipated)

March 30, 2019

Study Registration Dates

First Submitted

December 21, 2018

First Submitted That Met QC Criteria

December 21, 2018

First Posted (Actual)

December 26, 2018

Study Record Updates

Last Update Posted (Actual)

December 26, 2018

Last Update Submitted That Met QC Criteria

December 21, 2018

Last Verified

September 1, 2018

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2018SDU-QILU-8

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.

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