Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI

• Sponsor: Shandong University (Other)
• Overall Status: Unknown status
• CT.gov ID: NCT04136236
• Collaborator: (none)
• Enrollment: 60
• Location: 1
• Anticipated Duration (Months): 4
• Patients Per Site Per Month: 15

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Esophageal Neoplasms; Artificial Intelligence; Confocal Laser Endomicroscopy	Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopist

Study Design

• Study Type: Observational
• Anticipated Enrollment: 60 participants
• Observational Model: Cohort
• Time Perspective: Other
• Official Title: Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
• Actual Study Start Date: Aug 1, 2019
• Anticipated Primary Completion Date: Dec 1, 2019
• Anticipated Study Completion Date: Dec 1, 2019

Arms and Interventions

Arm	Intervention/Treatment
esophageal mucosal lesions observed by pCLE pCLE is used to distinguish the suspected lesions detected by white light endoscopy.	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.

Outcome Measures

Primary Outcome Measures

1. The diagnosis efficiency of Artificial Intelligence [3 month]

   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.

Secondary Outcome Measures

1. Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists [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.

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 80 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Qilu Hospital, Shandong University	Jinan	Shandong	China	250001

Sponsors and Collaborators

• Shandong University

Investigators

• Principal Investigator: Yanqing Li, Qilu Hospital, Shandong University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Yanqing Li, Vice president of QiLu Hospital, Shandong University
• ClinicalTrials.gov Identifier: NCT04136236
• Other Study ID Numbers: 2019SDU-QILU-66
• First Posted: Oct 23, 2019
• Last Update Posted: Oct 23, 2019
• Last Verified: Oct 1, 2019

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Additional relevant MeSH terms: Esophageal Neoplasms

Study Results

No Results Posted as of Oct 23, 2019