Back to resultsAutomatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
Primary Purpose
Probe-based Confocal Laser Endomicroscopy, Artificial Intelligence, Colorectal Polyps
Status
Unknown status
Phase
Not Applicable
Locations
China
Study Type
Interventional
Intervention
AI presentation
Sponsored by
About this trial
This is an interventional diagnostic trial for Probe-based Confocal Laser Endomicroscopy
Eligibility Criteria
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
Sites / Locations
- Endoscopic unit of Qilu Hospital Shandong UniversityRecruiting
Arms of the Study
Arm 1
Arm 2
Arm Type
Experimental
No Intervention
Arm Label
AI visible group
AI invisible group
Arm Description
Outcomes
Primary Outcome Measures
The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks
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.
Secondary Outcome Measures
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
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.
Full Information
NCT ID
NCT03787784
First Posted
December 21, 2018
Last Updated
December 21, 2018
Sponsor
Shandong University
1. Study Identification
Unique Protocol Identification Number
NCT03787784
Brief Title
Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
Official Title
Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
Study Type
Interventional
2. Study Status
Record Verification Date
September 2018
Overall Recruitment Status
Unknown status
Study Start Date
May 1, 2018 (Actual)
Primary Completion Date
January 30, 2019 (Anticipated)
Study Completion Date
March 30, 2019 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Shandong University
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
5. Study Description
Brief Summary
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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Probe-based Confocal Laser Endomicroscopy, Artificial Intelligence, Colorectal Polyps
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
ParticipantInvestigatorOutcomes Assessor
Allocation
Randomized
Enrollment
200 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
AI visible group
Arm Type
Experimental
Arm Title
AI invisible group
Arm Type
No Intervention
Intervention Type
Other
Intervention Name(s)
AI presentation
Intervention Description
Automatic diagnosis information of AI is visible to endoscopist
Primary Outcome Measure Information:
Title
The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks
Description
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.
Time Frame
4 months
Secondary Outcome Measure Information:
Title
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
Description
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.
Time Frame
3 month
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
80 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Yangqing Li, PHD.MD.
Phone
053182169385
Email
liyanqing@sdu.edu.cn
Facility Information:
Facility Name
Endoscopic unit of Qilu Hospital Shandong University
City
Jinan
State/Province
Shandong
ZIP/Postal Code
250001
Country
China
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Yanqing Li, PhD,MD
12. IPD Sharing Statement
Learn more about this trial
Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
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