The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinomaļ¼šA Single Center, Case-control, Diagnostic Study

Sponsor
Qilu Hospital of Shandong University (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT05819099
Collaborator
(none)
200
36

Study Details

Study Description

Brief Summary

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).

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning

Study Design

Study Type:
Observational
Anticipated Enrollment :
200 participants
Observational Model:
Case-Control
Time Perspective:
Retrospective
Official Title:
The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinomaļ¼šA Single Center, Case-control, Diagnostic Study
Anticipated Study Start Date :
Apr 1, 2023
Anticipated Primary Completion Date :
Apr 1, 2025
Anticipated Study Completion Date :
Apr 1, 2026

Arms and Interventions

Arm 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.

Outcome Measures

Primary Outcome Measures

  1. Sensitivity [36 months]

    The researchers calculated the sensitivity of the established AI model and compared it with endoscopists of different levels.

  2. Specificity [36 months]

    The researchers calculated the specificity of the established AI model and compared it with endoscopists of different levels.

  3. Negative predictive value [36 months]

    The researchers calculated the negative predictive value of the established AI model and compared it with endoscopists of different levels.

  4. Positive predictive value [36 months]

    The researchers calculated the positive predictive value of the established AI model and compared it with endoscopists of different levels.

  5. Accuracy [36 months]

    The researchers calculated accuracy positive predictive value of the established AI model and compared it with endoscopists of different levels.

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years to 75 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
Yes
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.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

  • Qilu Hospital of Shandong University

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Qilu Hospital of Shandong University
ClinicalTrials.gov Identifier:
NCT05819099
Other Study ID Numbers:
  • KYLL-202212-008
First Posted:
Apr 19, 2023
Last Update Posted:
Apr 19, 2023
Last Verified:
Apr 1, 2023
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of Apr 19, 2023