CClinicalTrials.gg
Status unknownNCT05459610Updated Jul 15, 2022

Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

An observational study in Intestinal Metaplasia of Gastric Mucosa, Artificial Intelligence and Endoscopy, sponsored by Shandong University. Status unknown at 1 site in China. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2022-07-15.

Sponsored by Shandong University · Observational

The sponsor has not verified this record recently (last verified Jul 2022), so the status shown — last known as Recruiting — may be out of date.
Study type
Observational
Model
Other
Time perspective
Retrospective
Enrollment
600
Ages
18 Years to 80 Years
Sex
All
01

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.

Read the detailed 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.

02

Conditions studied

  • Intestinal Metaplasia of Gastric Mucosa
  • Artificial Intelligence
  • Endoscopy

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03

In context

Metaplasia

99 studies on the registry are indexed under Metaplasia; 12 are open to participants now.

This study's planned enrollment of 600 is above the median of 253 across 48 observational studies indexed under Metaplasia.

Browse Metaplasia studies →

Lead sponsor

Shandong University is the lead sponsor of 284 studies on the registry; 59 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 80 Years
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Consecutive patients who receive the IEE examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study

Inclusion criteria

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

Exclusion criteria

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
05

Study design

Observational model
Other
Time perspective
Retrospective
Enrollment
600 participants (estimated)
Patient registry
No

Groups and cohorts

  • group for training the algorithm

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

  • group for testing the algorithm

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

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What researchers measure

Primary outcomes

  1. The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

    The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

    Time frame: 2 years

  2. The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

    The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

    Time frame: 2 years

  3. The sensitivity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

    The sensitivity of AI model to assess the degree of intestinal metaplasia in an

    Time frame: 2 years

Secondary outcomes

  1. Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia

    Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia in an endoscopic picture

    Time frame: 2 years

  2. Accuracy of the inexperienced endoscopists to assess the degree of intestinal metaplasia

    Accuracy of the inexperienced endoscopists to assess the degree of intestinal metaplasia in an endoscopic picture

    Time frame: 2 years

  3. Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia

    Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia in an endoscopic picture

    Time frame: 2 years

  4. Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia

    Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia in an endoscopic picture

    Time frame: 2 years

07

Study locations

1 of 1 sites recruiting
  • Department of Gastrology, QiLu Hospital, Shandong University
    Jinan, Shandong 250012, China
    Recruiting
08

References and documents

Individual participant data

Plan to share: No

No publications or documents are linked to this record.

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Jul 15, 2022, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT05459610
Lead sponsor
Shandong University
Responsible party
Yanqing Li (Vice President of Qilu Hospital, Shandong University) — Principal investigator
First posted
Jul 15, 2022
Start date
Jul 1, 2022
Primary completion
Dec 30, 2023 (estimated)
Completion
Dec 30, 2023 (estimated)
Last update
Jul 15, 2022

Study contacts

yanqing Li, MD, PHD
Contact
liyanqing@sdu.edu.cn
0531182169385
yanqing Li, MD, PHD
study chair · Qilu Hospital, Shandong University

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is status unknown, as verified in Jul 2022. You cannot join it, but the record below documents what was studied.

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