Automatic Real-time Diagnosis of Gastric Mucosal Disease Using pCLE With Artificial Intelligence

• Sponsor: Shandong University (Other)
• Overall Status: Completed
• CT.gov ID: NCT03784209
• Collaborator: (none)
• Enrollment: 951
• Location: 1
• Actual Duration (Months): 39
• Patients Per Site Per Month: 24.4

Study Details

Study Description

Brief Summary

Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.

Condition or Disease	Intervention/Treatment	Phase
Gastric Diseases; Artificial Intelligence; Confocal Laser Endomicroscopy	Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopist

Study Design

• Study Type: Observational
• Actual Enrollment: 951 participants
• Observational Model: Other
• Time Perspective: Prospective
• Official Title: Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
• Actual Study Start Date: Jul 1, 2018
• Actual Primary Completion Date: Sep 29, 2021
• Actual Study Completion Date: Sep 29, 2021

Arms and Interventions

Arm	Intervention/Treatment
lesions observed by pCLE pCLE is used to distinguish the suspected lesions detected by white light endoscopy.	Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopist; When suspected lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI.

Outcome Measures

Primary Outcome Measures

1. The diagnosis efficiency of Artificial Intelligence [24 months]

   The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing gastric mucosal disease on real-time pCLE examination.

Secondary Outcome Measures

1. Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists [24 months]

   The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing gastric mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 80 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Endoscopic unit of Qilu Hospital Shandong University	Jinan	Shandong	China	250001

Sponsors and Collaborators

• Shandong University

Investigators

• Principal Investigator: Yanqing Li, Qilu Hospital, Shandong University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Yanqing Li, Vice president of QiLu Hospital, Shandong University
• ClinicalTrials.gov Identifier: NCT03784209
• Other Study ID Numbers: 2018SDU-QILU-12
• First Posted: Dec 21, 2018
• Last Update Posted: Apr 1, 2022
• Last Verified: Mar 1, 2022

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Additional relevant MeSH terms: Stomach Diseases

Study Results

No Results Posted as of Apr 1, 2022