Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI

Completed

• Conditions: Artificial Intelligence; Confocal Laser Endomicroscopy; Esophageal Neoplasms
• Interventions: Diagnostic Test: The diagnosis of Artificial Intelligence and endoscopist

• Registration Number: NCT04136236
• Lead Sponsor: Shandong University

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2019-08-01
• Study First Submitted: 2019-10-20
• Study First Submitted QC: 2019-10-20
• Study First Posted: 2019-10-23
• Primary Completion: 2023-01-31
• Study Completion: 2023-01-31
• Status Verified: 2024-11
• Last Update Submitted: 2024-11-16
• Last Update Posted: 2024-11-19

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 57

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
esophageal mucosal lesions observed by pCLE	The diagnosis of Artificial Intelligence and endoscopist	pCLE is used to distinguish the suspected lesions detected by white light endoscopy.

Primary Outcome Measures

Name	Time	Method
The diagnosis efficiency of Artificial Intelligence	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.

Secondary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

Qilu Hospital, Shandong University; 🇨🇳 Jinan, Shandong, China