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

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.