Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI

November 16, 2024 updated by: Yanqing Li, Shandong University

Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

57

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China, 250001
        • Qilu Hospital, Shandong University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Consecutive patients who receive the upper gastrointestinal tract pCLE examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
esophageal mucosal lesions observed by pCLE
pCLE is used to distinguish the suspected lesions detected by white light endoscopy or IEE.
Diagnostic test: The diagnosis of Artificial Intelligence and endoscopist
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

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

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

This is where you will find people and organizations involved with this study.

Sponsor

Shandong University

Investigators

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

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2019

Primary Completion (Actual)

January 31, 2023

Study Completion (Actual)

January 31, 2023

Study Registration Dates

First Submitted

October 20, 2019

First Submitted That Met QC Criteria

October 20, 2019

First Posted (Actual)

October 23, 2019

Study Record Updates

Last Update Posted (Estimated)

November 19, 2024

Last Update Submitted That Met QC Criteria

November 16, 2024

Last Verified

November 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2019SDU-QILU-66

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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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