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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04136236
Other study ID # 2019SDU-QILU-66
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date August 1, 2019
Est. completion date December 1, 2019

Study information

Verified date October 2019
Source Shandong University
Contact Yanqing Li
Phone 053182169385
Email liyanqing@sdu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accurately.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 Recruiting
Enrollment 60
Est. completion date December 1, 2019
Est. primary completion date December 1, 2019
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; suspected esophageal mucosal lesion was found by white light endoscopy.

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

Study Design


Intervention

Diagnostic Test:
The diagnosis of Artificial Intelligence and endoscopist
When 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.

Locations

Country Name City State
China 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 esophageal mucosal disease on real-time pCLE examination. 3 month
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 esophageal mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists. 1 month
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