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

Artificial Intelligence Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05916014
• Other study ID #: 2022SDU-QILU-123
• Status: Recruiting
• Start date: June 1, 2023
• Est. completion date: December 31, 2024

Study information

• Verified date: April 2024
• Source: Shandong University
• Contact: yanqing Li, MD, PHD
• Phone: 0531182169385
• Email: liyanqing[@]sdu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1500
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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;

Related Conditions & MeSH terms

• Artificial Intelligence
• Atrophic Gastritis
• Atrophy
• Endoscopy
• Gastritis
• Gastritis, Atrophic

Intervention

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.

Locations

Country	Name	City	State
China	Department of Gastrology, QiLu Hospital, Shandong University	Shangdong	Shandong

Sponsors (2)

Lead Sponsor	Collaborator
Shandong University	Linyi County People's Hospital,Dezhou,China

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy of AI model to diagnose the Kimura-Takemoto classification	Accuracy of AI model to diagnose the Kimura-Takemoto classification	2 years
Primary	Sensitivity of AI model to diagnose the Kimura-Takemoto classification	Sensitivity of AI model to diagnose the Kimura-Takemoto classification	2 years
Primary	Specificity of AI model to diagnose the Kimura-Takemoto classification	Specificity of AI model to diagnose the Kimura-Takemoto classification	2 years
Secondary	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