Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

• Sponsor: Shandong University (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05459610
• Collaborator: (none)
• Enrollment: 600
• Location: 1
• Anticipated Duration (Months): 18
• Patients Per Site Per Month: 33.4

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Intestinal Metaplasia of Gastric Mucosa; Artificial Intelligence; Endoscopy

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 Design

• Study Type: Observational
• Anticipated Enrollment: 600 participants
• Observational Model: Other
• Time Perspective: Retrospective
• Official Title: Development and Validation of an Artificial Intelligence System for Automatic Evaluation of the Extent of Intestinal Metaplasia
• Actual Study Start Date: Jul 1, 2022
• Anticipated Primary Completion Date: Dec 30, 2023
• Anticipated Study Completion Date: Dec 30, 2023

Arms and Interventions

Arm	Intervention/Treatment
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

Outcome Measures

Primary Outcome Measures

1. The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture [2 years]

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

2. The accuracy 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

3. The sensitivity 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

Secondary Outcome Measures

1. Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia [2 years]

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

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

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

3. Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia [2 years]

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

4. Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia [2 years]

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

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 80 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Department of Gastrology, QiLu Hospital, Shandong University	Jinan	Shandong	China	250012

Sponsors and Collaborators

• Shandong University

Investigators

• Study Chair: yanqing Li, MD, PHD, Qilu Hospital, Shandong University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Yanqing Li, Vice President of Qilu Hospital, Shandong University
• ClinicalTrials.gov Identifier: NCT05459610
• Other Study ID Numbers: 2022SDU-QILU-109
• First Posted: Jul 15, 2022
• Last Update Posted: Jul 15, 2022
• Last Verified: Jul 1, 2022

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Additional relevant MeSH terms: Metaplasia

Study Results

No Results Posted as of Jul 15, 2022