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

Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment

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.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence; Endoscopy; Atrophic Gastritis
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Estimated): 1500

Contacts and Locations

• Study Contact: Name: yanqing Li, MD, PHD; Phone Number: 0531182169385; Email: liyanqing@sdu.edu.cn

Study Locations

• China
  • Shandong
    • Shangdong, Shandong, China, 250012
      • Recruiting
      • Department of Gastrology, QiLu Hospital, Shandong University
      • Contact: yanqing Li, MD, PHD; Phone Number: 0531182169385; Email: liyanqing@sdu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria at Qilu Hospital,Shandong University,Linyi County People's Hospital will be enrolled into the study

Description

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;

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of AI model to diagnose the Kimura-Takemoto classification	Accuracy of AI model to diagnose the Kimura-Takemoto classification	2 years
Sensitivity of AI model to diagnose the Kimura-Takemoto classification	Sensitivity of AI model to diagnose the Kimura-Takemoto classification	2 years
Specificity of AI model to diagnose the Kimura-Takemoto classification	Specificity of AI model to diagnose the Kimura-Takemoto classification	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture	The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture	2 years

Collaborators and Investigators

• Sponsor: Shandong University
• Collaborators: Linyi County People's Hospital,Dezhou,China
• Investigators: Study Chair: yanqing li, MD,PHD, Qilu Hospital, Shandong University

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2023
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: June 14, 2023
• First Submitted That Met QC Criteria: June 14, 2023
• First Posted (Actual): June 23, 2023

Study Record Updates

• Last Update Posted (Actual): April 12, 2024
• Last Update Submitted That Met QC Criteria: April 10, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases; Gastroenteritis; Pathological Conditions, Anatomical; Gastritis; Atrophy; Gastritis, Atrophic
• Other Study ID Numbers: 2022SDU-QILU-123

Drug and device information, study documents

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