Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

Last updated: July 14, 2022
Sponsor: Shandong University
Overall Status: Active - Recruiting

Phase

N/A

Condition

Precancerous Condition

Treatment

N/A

Clinical Study ID

NCT05459610
2022SDU-QILU-109
  • Ages 18-80
  • All Genders

Study Summary

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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

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

Exclusion

Exclusion Criteria:

  • patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatricdisorders who cannot participate in gastroscopy
  • patients with previous surgical procedures on the stomach
  • patients who refuse to sign the informed consent form

Study Design

Total Participants: 600
Study Start date:
July 01, 2022
Estimated Completion Date:
December 30, 2023

Study 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.

Connect with a study center

  • Department of Gastrology, QiLu Hospital, Shandong University

    Jinan, Shandong 250012
    China

    Active - Recruiting

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