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

Artificial Intelligence Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03787784
• Other study ID #: 2018SDU-QILU-8
• Status: Recruiting
• Phase: N/A
• 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
• 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

Related Conditions & MeSH terms

• Artificial Intelligence
• Colorectal Polyps
• Polyps
• Probe-based Confocal Laser Endomicroscopy

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