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Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

NCT05459610 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2022-07-15

No results posted yet for this 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.

Conditions

  • Intestinal Metaplasia of Gastric Mucosa
  • Artificial Intelligence
  • Endoscopy

Sponsors & Collaborators

  • Shandong University

    lead OTHER

Principal Investigators

  • yanqing Li, MD, PHD · Qilu Hospital, Shandong University

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-07-01
Primary Completion
2023-12-30
Completion
2023-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05459610 on ClinicalTrials.gov