Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

Shandong University

Status: Enrolling

Conditions: Artificial Intelligence; Endoscopy; Intestinal Metaplasia of Gastric Mucosa

Study type: Observational

Funder types: Other

Identifiers

NCT05459610

2022SDU-QILU-109

Details and patient eligibility

About

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.

Full description

Globally, gastric cancer is the fifth most prevalent malignancy and the third leading cause of cancer mortality. Gastric intestinal metaplasia (GIM) is an intermediate precancerous gastric lesion in the gastric cancer cascade. Studies have shown that the 5-year cumulative incidence of gastric cancer in IM patients ranges from 5.3% to 9.8% . 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.

Enrollment: 600 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• patients aged 18-80 years who undergo the IEE examination

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
• patients who refuse to sign the informed consent form

Trial design

600 participants in 2 patient groups

group for training the algorithm

Description:

This group of images is used for training the algorithm of the artificial intelligence

group for testing the algorithm

Description:

This group of images is used for testing the algorithm of the artificial intelligence