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

Study Overview
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Brief Summary
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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).

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

Conditions
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Stomach Neoplasms

Intervention / Treatment
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* Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training Set<br> | Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning<br>* 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.<br>|
| Test Set<br> | Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning<br>* 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.<br>|
| Verification Set<br> | Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

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