Automatic Real-time Diagnosis of Gastric Mucosal Disease Using pCLE With Artificial Intelligence
NCT ID: NCT03784209
Last Updated: 2022-04-01
Study Results
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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COMPLETED
951 participants
OBSERVATIONAL
2018-07-01
2021-09-29
Brief Summary
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Detailed Description
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Conditions
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Study Design
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OTHER
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
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.
Eligibility Criteria
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Inclusion Criteria
* agree to give written informed consent.
Exclusion Criteria
* Inability to provide informed consent
18 Years
80 Years
ALL
No
Sponsors
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Shandong University
OTHER
Responsible Party
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Yanqing Li
Vice president of QiLu Hospital
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
Countries
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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.
Other Identifiers
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2018SDU-QILU-12
Identifier Type: -
Identifier Source: org_study_id
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