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

NCT ID: NCT03784209

Last Updated: 2022-04-01

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

951 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-07-01

Study Completion Date

2021-09-29

Brief Summary

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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

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Conditions

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Gastric Diseases Artificial Intelligence Confocal Laser Endomicroscopy

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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lesions observed by pCLE

pCLE is used to distinguish the suspected lesions detected by white light endoscopy.

The diagnosis of Artificial Intelligence and endoscopist

Intervention Type 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.

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Shandong University

OTHER

Sponsor Role lead

Responsible Party

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Yanqing Li

Vice president of QiLu Hospital

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Yanqing Li

Role: PRINCIPAL_INVESTIGATOR

Qilu Hospital, Shandong University

Locations

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Endoscopic unit of Qilu Hospital Shandong University

Jinan, Shandong, China

Site Status

Countries

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China

References

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Liu G, Li G, Li Z, Shao X, Ji R, Ma T, Zhang Y, Su J, Qi Q, Guo J, He Y, Yang X, Li Y, Zuo X. Deep learning-aided optical biopsy achieves whole-chain diagnosis of Correa cascade of gastric cancer: a prospective study. BMC Med. 2025 Sep 30;23(1):527. doi: 10.1186/s12916-025-04310-9.

Reference Type DERIVED
PMID: 41029674 (View on PubMed)

Other Identifiers

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2018SDU-QILU-12

Identifier Type: -

Identifier Source: org_study_id

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