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

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.

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Key information

Conditions

Gastric Diseases Artificial Intelligence Confocal Laser Endomicroscopy Digestive System Diseases Gastrointestinal Diseases Stomach Diseases

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Endoscopic unit of Qilu Hospital Shandong University

Jinan, Shandong, 250001, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

The diagnosis of Artificial Intelligence and endoscopist

Diagnostic Test

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.

Primary outcomes

The diagnosis efficiency of Artificial Intelligence

Time frame: 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 outcomes

Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists

Time frame: 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.

Sponsors and collaborators

Lead sponsor

Shandong University

Other

Registry information

Official study title

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

Important dates

Study start: 2018
Primary completion: 2021
Study completion: 2021
First posted: Dec 21, 2018
Registry last updated: Apr 1, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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