AI-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis

• Sponsor: Shandong University (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05916014
• Collaborator: (none)
• Enrollment: 1,500
• Location: 1
• Anticipated Duration (Months): 19
• Patients Per Site Per Month: 78.9

Study Details

Study Description

Brief Summary

Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.

Condition or Disease	Intervention/Treatment	Phase
Atrophic Gastritis; Artificial Intelligence; Endoscopy	Diagnostic Test: Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists

Detailed Description

Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. The higher the score, the more severe the degree of atrophic gastritis. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification of atrophic gastritis to achieve gastric cancer risk assessment.

Study Design

• Study Type: Observational
• Anticipated Enrollment: 1500 participants
• Observational Model: Other
• Time Perspective: Prospective
• Official Title: Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment
• Actual Study Start Date: Jun 1, 2023
• Anticipated Primary Completion Date: Dec 31, 2024
• Anticipated Study Completion Date: Dec 31, 2024

Arms and Interventions

Arm	Intervention/Treatment
Chronic atrophic gastritis observed by white light endoscope Get pictures from gastric antrum,gastric angle,lesser curvature of gastric body, cardia, gastric fundus, greater curvature of gastric body by white light endoscope	Diagnostic Test: Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists; Endosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible.

Outcome Measures

Primary Outcome Measures

1. Accuracy of AI model to diagnose the Kimura-Takemoto classification [2 years]

   Accuracy of AI model to diagnose the Kimura-Takemoto classification

2. Sensitivity of AI model to diagnose the Kimura-Takemoto classification [2 years]

   Sensitivity of AI model to diagnose the Kimura-Takemoto classification

3. Specificity of AI model to diagnose the Kimura-Takemoto classification [2 years]

   Specificity of AI model to diagnose the Kimura-Takemoto classification

Secondary Outcome Measures

1. The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture [2 years]

   The MIOU value of AI model in semantic segmentation of endoscopic atrophy 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 white light endoscope examination Informed consent form provided by the patient.

Exclusion Criteria:

1. patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric;

2. disorders who cannot participate in gastroscopy;

3. Patients with progressive gastric cancer;

4. low quality pictures;

5. patients with previous surgical procedures on the stomach or esophageal;

6. 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	Shangdong	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: NCT05916014
• Other Study ID Numbers: 2022SDU-QILU-123
• First Posted: Jun 23, 2023
• Last Update Posted: Jun 23, 2023
• Last Verified: Jun 1, 2023

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Additional relevant MeSH terms: Gastritis; Gastritis, Atrophic; Atrophy

Study Results

No Results Posted as of Jun 23, 2023