Validation the Artificial Intelligence System for Automatic Evaluation of the Extent of Intestinal Metaplasia

Last updated: November 27, 2024
Sponsor: Shandong University
Overall Status: Completed

Phase

N/A

Condition

Precancerous Condition

Treatment

The diagnosis of Artificial Intelligence and endosopists

Clinical Study ID

NCT05464108
2022SDU-QILU-111
  • Ages 40-75
  • All Genders

Study Summary

The operative link on gastric intestinal metaplasia assessment (OLGIM) staging systems using biopsy specimens were commonly used for histological assessment of gastric cancer risk. But its clinical application is limited for at least biopsy samples. The endoscopic grading system (EGGIM) has been shown a significant correlation with the OLGIM. The investigators designed a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores in endoscopy examination. This study is aimed at exploring the relevance of the EGGIM scores automatically evaluated by Artificial Intelligence and OLGIM scores.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • patients aged 40-75 years who undergo the IEE examination

Exclusion

Exclusion Criteria:

  • patients with severe cardiac, cerebral, pulmonary or renal dysfunction orpsychiatric

  • disorders who cannot participate in gastroscopy

  • patients with previous surgical procedures on the stomach

  • patients with contraindications to biopsy

  • patients who refuse to sign the informed consent form

Study Design

Total Participants: 1080
Treatment Group(s): 1
Primary Treatment: The diagnosis of Artificial Intelligence and endosopists
Phase:
Study Start date:
July 01, 2022
Estimated Completion Date:
December 30, 2023

Study Description

Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). The operative link on gastric intestinal metaplasia assessment (OLGIM) staging systems using biopsy specimens were commonly used for histological assessment of gastric cancer risk. However, its need to take at least 4 biopsies is not clinically feasible. The endoscopic grading system (EGGIM) has been shown a significant correlation with the OLGIM. An EGGIM score of 5 was the best cut off value for identifying OLGIM stage III/IV patients. The investigators have designed a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores in endoscopy examination. This study is aimed at exploring the relevance of the EGGIM scores automatically evaluated by Artificial Intelligence and OLGIM scores.

Connect with a study center

  • Department of Gastrology, QiLu Hospital, Shandong University

    Jinan, Shandong 250012
    China

    Site Not Available

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