NCT03787784

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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
200

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started May 2018

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

May 1, 2018

Completed
8 months until next milestone

First Submitted

Initial submission to the registry

December 21, 2018

Completed
5 days until next milestone

First Posted

Study publicly available on registry

December 26, 2018

Completed
1 month until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 30, 2019

Completed
2 months until next milestone

Study Completion

Last participant's last visit for all outcomes

March 30, 2019

Completed
Last Updated

December 26, 2018

Status Verified

September 1, 2018

Enrollment Period

9 months

First QC Date

December 21, 2018

Last Update Submit

December 21, 2018

Conditions

Probe-based Confocal Laser EndomicroscopyArtificial IntelligenceColorectal Polyps

Outcome Measures

Primary Outcomes (1)

  • 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.

    4 months

Secondary Outcomes (1)

  • Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists

    3 month

Study Arms (2)

AI visible group

EXPERIMENTAL
Other: AI presentation

AI invisible group

NO INTERVENTION

Interventions

AI presentationOTHER

Automatic diagnosis information of AI is visible to endoscopist

AI visible group

Eligibility Criteria

Age18 Years - 80 Years
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Endoscopic unit of Qilu Hospital Shandong University

Jinan, Shandong, 250001, China

RECRUITING

Central Study Contacts

Yangqing Li, PHD.MD.

CONTACT

053182169385liyanqing@sdu.edu.cn

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
TRIPLE
Who Masked
PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Vice president of QiLu Hospital

Study Record Dates

First Submitted

December 21, 2018

First Posted

December 26, 2018

Study Start

May 1, 2018

Primary Completion

January 30, 2019

Study Completion

March 30, 2019

Last Updated

December 26, 2018

Record last verified: 2018-09

Locations

CN(1)