The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma：A Single Center, Case-control, Diagnostic Study

November 15, 2023 updated by: Qilu Hospital of Shandong University
This is a single center, case-control, diagnostic study.The aim of this study is to use deep learning methods to retrospectively analyze the imaging data of gastrointestinal endoscopy in Qilu Hospital, and construct an artificial intelligence model based on endoscopic images for detecting and determining the depth of invasion of esophagogastric junctional adenocarcinoma.This study will also compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.The research includes stages such as data collection and preprocessing, artificial intelligence model development, model testing and evaluation. The gastroscopy image dataset constructed by this research institute mainly includes three modes of endoscopic imaging: white light endoscopy, optical enhancement endoscopy (OE), and narrowband imaging endoscopy (NBI).

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

200

Contacts and Locations

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

Study Contact

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

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

This study included endoscopic images of the esophageal gastric junction retrieved from the Endoscopy Center of Qilu Hospital for training and testing the model.The study population underwent pathological examination and the pathological results were used as the gold standard.

Description

Inclusion Criteria:

  • This study included endoscopic images of patients aged 18 and above who underwent endoscopic examination or treatment
  • All patients in the case group need to be pathologically confirmed as esophageal gastric junction adenocarcinoma, and a pathologist has conducted a standardized pathological evaluation of the tumor classification of the lesion, including the overall appearance, size, differentiation type, depth of infiltration, presence or absence of lymphatic/vascular invasion, surgical margin status, etc.
  • The endoscopic images of the control group patients need to be confirmed by biopsy pathology or at least two experienced endoscopists (with operating experience>5000 cases) to jointly confirm that they have clear benign manifestations

Exclusion Criteria:

  • The patient has a previous history of endoscopic treatment or surgery for the esophageal gastric junction.
  • Necessary clinical information cannot be provided during the research process (patient age, gender, lesion characteristics, endoscopic manifestations, endoscopic images, etc.)
  • Low quality endoscopic images, such as those severely affected by bleeding, aperture, blurring, defocusing, artifacts, or excessive mucus after biopsy.

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
Training Set
Diagnostic test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning
This study will compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.
Test Set
Diagnostic test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning
This study will compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.
Verification Set
Diagnostic test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning
This study will compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity
Time Frame: 36 months
The researchers calculated the sensitivity of the established AI model and compared it with endoscopists of different levels.
36 months
Specificity
Time Frame: 36 months
The researchers calculated the specificity of the established AI model and compared it with endoscopists of different levels.
36 months
Negative predictive value
Time Frame: 36 months
The researchers calculated the negative predictive value of the established AI model and compared it with endoscopists of different levels.
36 months
Positive predictive value
Time Frame: 36 months
The researchers calculated the positive predictive value of the established AI model and compared it with endoscopists of different levels.
36 months
Accuracy
Time Frame: 36 months
The researchers calculated accuracy positive predictive value of the established AI model and compared it with endoscopists of different levels.
36 months

Collaborators and Investigators

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

Sponsor

Qilu Hospital of 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 (Estimated)

December 1, 2023

Primary Completion (Estimated)

April 1, 2025

Study Completion (Estimated)

April 1, 2026

Study Registration Dates

First Submitted

April 6, 2023

First Submitted That Met QC Criteria

April 6, 2023

First Posted (Actual)

April 19, 2023

Study Record Updates

Last Update Posted (Estimated)

November 16, 2023

Last Update Submitted That Met QC Criteria

November 15, 2023

Last Verified

April 1, 2023

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2023SDU-QILU-1

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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