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

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ClinicalTrials.gov Identifier: NCT03787784
Recruitment Status : Recruiting
First Posted : December 25, 2018
Last Update Posted : December 25, 2018
Sponsor:
Information provided by (Responsible Party):
Yanqing Li, Shandong University

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.

Condition or disease Intervention/treatment Phase
Probe-based Confocal Laser Endomicroscopy Artificial Intelligence Colorectal Polyps Other: AI presentation Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 200 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Triple (Participant, Investigator, Outcomes Assessor)
Primary Purpose: Diagnostic
Official Title: Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence
Actual Study Start Date : May 1, 2018
Estimated Primary Completion Date : January 30, 2019
Estimated Study Completion Date : March 30, 2019

Arm Intervention/treatment
Experimental: AI visible group Other: AI presentation
Automatic diagnosis information of AI is visible to endoscopist

No Intervention: AI invisible group



Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. 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.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
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


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03787784


Contacts
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Contact: Yangqing Li, PHD.MD. 053182169385 liyanqing@sdu.edu.cn

Locations
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China, Shandong
Endoscopic unit of Qilu Hospital Shandong University Recruiting
Jinan, Shandong, China, 250001
Contact: Yanqing Li, PhD,MD         
Sponsors and Collaborators
Shandong University

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Responsible Party: Yanqing Li, Vice president of QiLu Hospital, Shandong University
ClinicalTrials.gov Identifier: NCT03787784     History of Changes
Other Study ID Numbers: 2018SDU-QILU-8
First Posted: December 25, 2018    Key Record Dates
Last Update Posted: December 25, 2018
Last Verified: September 2018

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
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Polyps
Pathological Conditions, Anatomical