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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT03787784
Other study ID # 2018SDU-QILU-8
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
Start date May 1, 2018
Est. completion date March 30, 2019

Study information

Verified date September 2018
Source Shandong University
Contact Yangqing Li, PHD.MD.
Phone 053182169385
Email liyanqing@sdu.edu.cn
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial 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.


Recruitment information / eligibility

Status Recruiting
Enrollment 200
Est. completion date March 30, 2019
Est. primary completion date January 30, 2019
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years to 80 Years
Eligibility 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 Design


Related Conditions & MeSH terms


Intervention

Other:
AI presentation
Automatic diagnosis information of AI is visible to endoscopist

Locations

Country Name City State
China Endoscopic unit of Qilu Hospital Shandong University Jinan Shandong

Sponsors (1)

Lead Sponsor Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary 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 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. 3 month
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