Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). With technical advance of image-enhanced endoscopy (IEE), studies have demonstrated IEE has high accuracy for diagnosis of GIM. The endoscopic grading system (EGGIM), a new endoscopic risk scoring system for GC, have been shown to accurately identify a wide range of patients with GIM. However, the high diagnostic accuracy of GIM using IEE and EGGIM assessments performed all require much experience, which limits the application of EGGIM. The investigators aim to design a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
Timeframe: 2 years
The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
Timeframe: 2 years
The sensitivity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
Timeframe: 2 years