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

Completed

• Conditions: Gastric Diseases; Confocal Laser Endomicroscopy; Artificial Intelligence
• Interventions: Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopist

• Registration Number: NCT03784209
• Lead Sponsor: Shandong University

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2018-07-01
• Study First Submitted: 2018-12-16
• Study First Submitted QC: 2018-12-19
• Study First Posted: 2018-12-21
• Primary Completion: 2021-09-29
• Study Completion: 2021-09-29
• Status Verified: 2022-03
• Last Update Submitted: 2022-03-20
• Last Update Posted: 2022-04-01

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 951

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

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
lesions observed by pCLE	The diagnosis of Artificial Intelligence and endoscopist	pCLE is used to distinguish the suspected lesions detected by white light endoscopy.

Primary Outcome Measures

Name	Time	Method
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

Name	Time	Method
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.

Trial Locations

Locations (1)

Endoscopic unit of Qilu Hospital Shandong University; 🇨🇳 Jinan, Shandong, China