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

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).