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

Artificial Intelligence Clinical Trial

Official title:

Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03784209
• Other study ID #: 2018SDU-QILU-12
• Status: Completed
• Start date: July 1, 2018
• Est. completion date: September 29, 2021

Study information

• Verified date: March 2022
• Source: Shandong University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 951
• Est. completion date: September 29, 2021
• Est. primary completion date: September 29, 2021
• Accepts healthy volunteers: No
• 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
• Confocal Laser Endomicroscopy
• Gastric Diseases
• Stomach Diseases

Intervention

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.

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 diagnosis efficiency of Artificial Intelligence	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.	24 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 gastric mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.	24 months