Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

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

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.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence; Endoscopy; Intestinal Metaplasia of Gastric Mucosa

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.

• Study Type: Observational
• Enrollment (Anticipated): 600

Contacts and Locations

Study Locations

• China
  • Shandong
    • Jinan, Shandong, China, 250012
      • Recruiting
      • Department of Gastrology, QiLu Hospital, Shandong University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Retrospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2 years
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	2 years
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	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2 years
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	2 years
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	2 years
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	2 years

Collaborators and Investigators

• Sponsor: Shandong University

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2022
• Primary Completion (Anticipated): December 30, 2023
• Study Completion (Anticipated): December 30, 2023

Study Registration Dates

• First Submitted: July 8, 2022
• First Submitted That Met QC Criteria: July 14, 2022
• First Posted (Actual): July 15, 2022

Study Record Updates

• Last Update Posted (Actual): July 15, 2022
• Last Update Submitted That Met QC Criteria: July 14, 2022
• Last Verified: July 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Metaplasia
• Other Study ID Numbers: 2022SDU-QILU-109

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No