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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: yanqing Li, MD, PHD
- Phone Number: 0531182169385
- Email: liyanqing@sdu.edu.cn
Study Locations
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Shandong
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Shangdong, Shandong, China, 250012
- Recruiting
- Department of Gastrology, QiLu Hospital, Shandong University
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Contact:
- yanqing Li, MD, PHD
- Phone Number: 0531182169385
- Email: liyanqing@sdu.edu.cn
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient.
Exclusion Criteria:
- patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric;
- disorders who cannot participate in gastroscopy;
- Patients with progressive gastric cancer;
- low quality pictures;
- patients with previous surgical procedures on the stomach or esophageal;
- patients who refuse to sign the informed consent form;
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / 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
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Endosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Accuracy of AI model to diagnose the Kimura-Takemoto classification
Time Frame: 2 years
|
Accuracy of AI model to diagnose the Kimura-Takemoto classification
|
2 years
|
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Sensitivity of AI model to diagnose the Kimura-Takemoto classification
Time Frame: 2 years
|
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
|
2 years
|
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Specificity of AI model to diagnose the Kimura-Takemoto classification
Time Frame: 2 years
|
Specificity of AI model to diagnose the Kimura-Takemoto classification
|
2 years
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture
Time Frame: 2 years
|
The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture
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2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Study Chair: yanqing li, MD,PHD, Qilu Hospital, Shandong University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2022SDU-QILU-123
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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