The endoscopic grading system (EGGIM) has been widely used to assess the extent of gastric intestinal metaplasia during endoscopy. Investigators developed an artificial intelligence (AI) system to automatically evaluate the extent of gastric intestinal metaplasia (GIM) and calculate the EGGIM scores in endoscopy examination. This study is a prospective, multi-center study aimed at exploring the performance and reliability of AI-EGGIM scoring.

This is a prospective study designed to validate the AI-EGGIM system in a larger cohort. The study protocol was developed based on preliminary experience from a prior investigation (NCT05464108).

Gastric intestinal metaplasia (GIM) is an important precancerous stage in the gastric carcinogenesis cascade. The endoscopic grading system (EGGIM) has been proposed as a practical method to evaluate the extent of GIM during endoscopy. Investigators developed an artificial intelligence (AI) system to automatically assess the extent of GIM and calculate EGGIM scores from endoscopic examinations. This study is a prospective, multi-center study aimed at evaluating the accuracy, performance, and reliability of AI-assisted EGGIM scoring.

Inclusion Criteria:

• patients aged 40-75 years who undergo the IEE examination
• patients who voluntarily sign the informed consent form

Exclusion Criteria:

• patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
• patients with previous surgical procedures on the stomach

Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria will be enrolled into the study.