Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI
Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
-
-
Shandong
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Jinan, Shandong, China, 250001
- Qilu Hospital, Shandong University
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- aged between 18 and 80;
- agree to give written informed consent;
Exclusion Criteria:
- advanced esophageal squamous cell carcinoma or esophageal stenosis;
- having no suspicious lesion of ESN found by WLE and IEE
- known allergy to fluorescein sodium;
- having coagulopathy or impaired renal function;
- being pregnant or breastfeeding.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
esophageal mucosal lesions observed by pCLE
pCLE is used to distinguish the suspected lesions detected by white light endoscopy or IEE.
|
Suspected esophageal mucosal lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently.
In addition, the endoscopist can not see the diagnosis of AI.
After a washout period, nonexpert endoscopists take the second assessment with AI assistance.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The diagnosis efficiency of Artificial Intelligence
Time Frame: 3 years
|
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing esophageal mucosal disease on real-time pCLE examination.
|
3 years
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
Time Frame: 1 month
|
The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing esophageal mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.
|
1 month
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Yanqing Li, Qilu Hospital, Shandong University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Estimated)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2019SDU-QILU-66
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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